u/Paulinefoster

My company's website has a DA of 81 on Moz and we used to get millions of traffic per month but then something happened and we have been struggling with the traffic since the past 1 year.

There is no manual flagging from Google but we have a hypothesis that we might be shadowbanned by Google

Trying to figure out how to get things back on track, any suggestions?

Google is about to launch Information Agents that monitor web changes 24/7 and read everything then summarize it back to you. AI Overviews already ate half the click-through, and once this rolls out publishers' traffic will probably get squeezed even more. What do you think, will it keep eating web traffic?

Google is about to launch Information Agents that monitor web changes 24/7 and read everything then summarize it back to you. AI Overviews already ate half the click-through, and once this rolls out publishers' traffic will probably get squeezed even more. What do you think, will it keep eating web traffic?

The era of the “ten blue links” is officially over.

At its Google I/O conference on Tuesday, Google unveiled an AI-powered overhaul of Search centered around a reimagined “intelligent search box” — what the company describes as the biggest change to this entry point to the web since the search box debuted more than 25 years ago.

Instead of returning a simple list of links, Google Search will drop users into AI-powered interactive experiences at times. Google is also introducing tools that can dispatch “information agents” to gather information on a user’s behalf, along with tools that let users build personalized mini apps tailored to their needs.

The resulting experience will no longer look much like how people envision Google Search, which has long been defined by ranked links to websites that have the information you need.

With the revamped Search experience, the new search box simply expands to accommodate longer, more conversational queries, rather than making you decide what type of search experience or mode you want to choose at the start of your query. It will also have a new AI-powered query suggestion system that goes beyond autocomplete to help people craft more complex and nuanced queries, Google says.

Google’s AI Overviews will also allow users to ask follow-up questions in AI Mode, beginning Tuesday, the company noted.

Google is also introducing agentic capabilities and AI-powered interactive features into the search experience. This means people will spend even less time clicking the traditional blue links that Google Search used to return.

Starting this summer, people will be able to create, customize, and manage multiple new “information agents” within Google Search. These agents can work in the background 24/7 to track changes on the web and alert you to new information. For instance, you could have an agent track market movements based on customer parameters, Google suggests.

While the underlying technology here is powered by AI, which makes it more capable, the idea itself is not a new one.

In 2003, Google launched Google Alerts, a change-detection service that emailed users when new web results matched their search terms. The web was smaller and more manageable then, of course, so this became a part of many information workers’ tool sets. (That service still exists in some form but is no longer the way most web users go about acquiring new information.)

Information-gathering agents are an evolution of Google Alerts. Beyond spotting changes, they can make sense of them, too.

“You could send an alert to track market movements in a particular sector with very specific parameters, and the agent will map out a monitoring plan for you, including the tools and the data it needs to access — like our real-time finance data,” Google’s head of Search, Liz Reid, explained in a press briefing. “And it will then keep track of those changes and let you know when the conditions are met, and provide a synthesized update with links and information you can dive into further,” she added.

This shift means that “searching the web” will increasingly be performed by AI agents rather than humans. Instead, people will focus more on acting on the information those agents provide instead of manually clicking links.

Links will become an afterthought with the coming changes to the Search results experience, which builds on Google’s earlier launches of AI search features, like its short summaries known as AI Overviews and its conversational search, AI Mode.

AI Overviews are now used by more than 2.5 billion monthly users; meanwhile, its conversational search mode, launched last year, now tops 1 billion monthly users. (ChatGPT, for comparison, has 900 million weekly active users, as of earlier this year. This suggests that ChatGPT is now seeing more frequent engagement, with users coming back repeatedly throughout the week, while Google has more total unique people touching its AI features over the course of a month.)

Now, thanks to a combination of Gemini and Google Antigravity, the company’s agentic development platform, Search results will begin to look more like interactive web pages.

“Search can build custom experiences just for your individual questions, from dynamic layouts, interactive visuals to persistent and stateful project spaces that you can return to again and again,” says Reid. One of the ways Google is integrating these new capabilities is with “generative UI” (user interface), where it builds custom widgets and visualizations on the fly in answer to users’ search questions.

You can imagine, for example, how a question about black holes in space could lead to an interactive visual that brings the concept to life, Reid said, adding that users can then ask follow-up questions and see Google respond with brand-new visuals in real time.

Google says the new system was built in partnership with the Google DeepMind team and uses Gemini Flash 3.5. It will roll out to everyone who uses Google, free of charge, this summer.

In addition, Google will allow users to tap into Antigravity to build their own customizable, stateful experiences — think “mini apps” — directly in Search using natural-language commands. Again, this isn’t so much about information retrieval as it is about action. For instance, you could build a meal-planning app using information from your own calendar to help you decide what to prep and when to eat, or a fitness app created for your specific goals.

Combined, these changes will likely further decimate Google referrals to publishers, which have already been suffering from declining referrals due to AI Overviews. This has put some ad-dependent media operations out of business, and now things will likely get worse.

There’s little time left for publishers to adapt. The new search box is arriving this week, and generative UI is arriving this summer. Both are free. The mini-app-building feature and information agents will roll out first to Google AI Pro and Ultra subscribers this summer.

But Google’s long-term plan is to make its AI technology more broadly accessible, including its personal AI agent Spark, which will eventually be free, as will many of the AI features.

“Part of the reason we focus on delivering frontier models — highly capable, but also very efficient, fast, and at a lower price — is because we want to bring it to as many people as possible, and so I think that’s an area where we will shine,” Google CEO Sundar Pichai said in a press briefing ahead of I/O.

Google has added markdown files, .md.txt files, to the Google Search help documents. But John Mueller from Google said that these are not being used for Search or generative AI responses in Search.

John Mueller from Google responded to this on LinkedIn and said, "This is not being done for Search or generative AI responses in Search."

Here is the markdown file for that document, if you cannot access it yourself.

This reminds me of when Google added the LLMS.txt files to their help docs and then removed it and said it does not endorse LLMS.txt.

I guess we will see where this goes but Google is saying, even though Markdown files are available, Google Search does not use it. It could be used for many othe reasons outside of Search.

Forum discussion at LinkedIn.

I think the question of how products get recommended by AI should actually be split into two parts:

1. How does a product enter the AI's recommendation list in the first place

2. Once it's in the list, what kind of information can AI provide when the user keeps asking follow-up questions

What everyone is asking right now is basically the first one.

My thinking here is the same as how I used to approach keyword work. Among the sources that AI cites, some pages are extremely hard to compete with, while others are not. It's the same as keyword ranking difficulty: you can start with the easier ones, secure coverage on content you can actually win, and then gradually move toward the pages that are harder to displace, making sure you have a baseline of coverage.

I spent some time doing PSEO and found that as long as the keyword choice is right—for example, placing it within a topic that has very little competition—the content can continue to be cited by AI. On top of that, these lower-difficulty pages also help the higher-difficulty pages rank: the AI's scoring system gives these pages a higher consistency score, which raises the probability of being cited overall.

As for the second question, when users dig deeper into your product, you need to make sure the product information is presented to them in full. This is really the second step you should be working on:

Publishing detailed write-ups of your product on platforms like Reddit, blogs, and G2. Don't just promote the product. Honestly analyze its individual features, the actual usage experience, and the pros and cons compared with other tools.

Of course, this also requires attention to keyword and query technique.

I’ve noticed a new claim floating around lately: some people are starting to say that using screenshots instead of generic stock images will increase "AI trust" in your website.

We already know that Google encourages original images as part of your content. But what about AI? AI doesn't actually "see" or recognize an image the way a human does; it basically slices the image into tokens to process and read it.

Honestly, I’m highly skeptical of this whole "AI trust" narrative. From my perspective, I don't think AI models even possess a functional mechanism that equates to "trust" in the way these people are claiming.

When it comes to visuals, I strongly believe their primary role is to help humans better understand the context—which aligns perfectly with Google’s emphasis on "people-first content."

I really don't think Google (or any Generative Engine) is going to give your site a magical ranking boost just because you used an original screenshot instead of a stock photo.

What are your thoughts on this? Is "AI trust" an actual thing we should be optimizing for, or is this just another GEO superstition? Would love to hear your takes!

I’ve noticed a new claim floating around lately: some people are starting to say that using screenshots instead of generic stock images will increase "AI trust" in your website.

We already know that Google encourages original images as part of your content. But what about AI? AI doesn't actually "see" or recognize an image the way a human does; it basically slices the image into tokens to process and read it.

Honestly, I’m highly skeptical of this whole "AI trust" narrative. From my perspective, I don't think AI models even possess a functional mechanism that equates to "trust" in the way these people are claiming.

When it comes to visuals, I strongly believe their primary role is to help humans better understand the context—which aligns perfectly with Google’s emphasis on "people-first content."

I really don't think Google (or any Generative Engine) is going to give your site a magical ranking boost just because you used an original screenshot instead of a stock photo.

What are your thoughts on this? Is "AI trust" an actual thing we should be optimizing for, or is this just another GEO superstition? Would love to hear your takes!

I’ve noticed a new claim floating around lately: some people are starting to say that using screenshots instead of generic stock images will increase "AI trust" in your website.

We already know that Google encourages original images as part of your content. But what about AI? AI doesn't actually "see" or recognize an image the way a human does; it basically slices the image into tokens to process and read it.

Honestly, I’m highly skeptical of this whole "AI trust" narrative. From my perspective, I don't think AI models even possess a functional mechanism that equates to "trust" in the way these people are claiming.

When it comes to visuals, I strongly believe their primary role is to help humans better understand the context—which aligns perfectly with Google’s emphasis on "people-first content."

I really don't think Google (or any Generative Engine) is going to give your site a magical ranking boost just because you used an original screenshot instead of a stock photo.

What are your thoughts on this? Is "AI trust" an actual thing we should be optimizing for, or is this just another GEO superstition? Would love to hear your takes!

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

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

LLMS txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.

"Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.

Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.

Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.

Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

More info in Google’s blog:ai-optimization-guide

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

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

LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.

"Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.

Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.

Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.

Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

After several years of declining search traffic, Condé Nast CEO

has directed all the company's brands to operate as if search traffic to their properties will be zero.

He says the era of turning search and social media traffic into profitable businesses is gone.

And that if you run a media business that doesn't have an authoritative brand, a very strong niche, or a direct audience, you're going to be fighting hostile algo changes all the way down.

He describes a recent board meeting:

"We took a snapshot of search results from seven or eight years ago. And what you saw were a few sponsored links, then the ten blue links."

"Do the same search today, you get an AI overview, then you get rows and rows and rows of commerce links, then you get sponsored stuff."

"Each of the last three years, we would do our budgets, and we'd put forecasts in of search traffic declining. Because we'd seen the pattern of algorithm changes. And generally those algorithm changes were negative."

"Every year, our search traffic was down more than we had forecast. So last year I told our teams, 'Assume there's no search.' You have to have your businesses planned as if search is zero. We don't expect it to be zero, we expect it to be a single-digit percentage of our traffic."

After digging through the Reddit vs Perplexity lawsuit, I found a pretty interesting example.

To prove that Perplexity was indirectly getting Reddit data through Google, Reddit set up what they called a “honeypot test,” basically the digital version of marked bills.

The idea was simple: Reddit created test posts that could only be accessed by Google’s search crawler. A few hours after those pages were indexed by Google, the content from those test posts started appearing inside Perplexity answer queries.

So Perplexity apparently was not building its own search engine for this. Instead, it was buying services from third-party providers and indirectly getting Reddit data that had already been crawled by Google.

My guess is that a lot of other AI companies work similarly too. They mostly rely on Google’s data layer to build answers.

The original filing mentions this on page 27.

Lately there’s a lot of discussion around llms.txt, with some calling it the new robots.txt for AI crawlers.

Some say adding it helps LLMs better understand website content and improves visibility in tools like ChatGPT, Perplexity, or Gemini. Others think it’s mostly hype and not something that makes a measurable difference right now.

Has anyone actually tested this and seen real impact? Curious whether adding llms.txt is becoming important for AI visibility or if it’s still too early to matter.

This is a rough summary of public discussions and claims about how GPT-5.2-era ChatGPT may have retrieved information from webpages.

I’m writing it mainly as a reference point for GPT-5.5, since OpenAI no longer shares much detail about what happens behind the scenes. One important caveat: the model names mentioned below were internal labels observed during the GPT-5.2 period. They are not from official OpenAI documentation. Whether GPT-5.5 still uses anything similar is unknown, so this should be treated as uncertain and speculative.

The basic idea is that GPT-5.2 probably was not directly fetching and processing webpages by itself. It seemed to rely on other routing, retrieval, and filtering components first. Those components handled the search process, selected useful information, and then passed the processed context back to GPT-5.2 for the final answer.

The starting point is simple: web retrieval is expensive. Searching, fetching, parsing, filtering, and reasoning over webpages costs much more than answering from the model’s own knowledge.

Because of cost and response speed, it makes sense that ChatGPT would not read a large number of webpages for most queries. There are also public estimates suggesting that ChatGPT now triggers web search far less often than people assume. I have seen the “around one-third of queries” number mentioned, but I would not treat that as settled. The broader point is that many queries are still answered directly from training data, without live webpage retrieval.

So the idea that ChatGPT “doesn’t read that many pages” is probably right. But the reason is not only cost. The model also seems more willing to answer from its own knowledge first.

Based on the public claims I found, when GPT-5.2 handled a web-connected query, one internal label that appeared was snc-pg-sw-3cls-ev3. This component was described as deciding whether web browsing was needed and how complex the search should be.

If the query was classified as needing web search, another label appeared: alpha.sonic_thinky_v1.

This component seemed to handle query planning. In other words, it decided how deep the search should go, then split and rewrote the user’s original question into different subqueries.

Sometimes one simple query may be enough. Other times, the system may decide the request needs a more complex search and generate multiple queries. That may explain why some web answers return quickly, while others feel much slower. The delay could be the query-planning step showing up.

After the search results come back, the system appears to filter them heavily. Most pages are dropped, and only a small group of candidate pages remain.

Duplicate links may be removed. Pages that load too slowly may also be excluded.

There is one detail worth being careful with: some people mention that the system may keep “10–20 pages,” but the source for that number is unclear. OpenAI has not publicly confirmed it, so I would treat it as a rough estimate, not a fact.

After that, the candidate pages are parsed and split. I do not want to overstate the exact technical details here. The rough process seems to be: process the pages in batches, break them into token chunks, compare and score those chunks, then decide which tokens are useful and which ones should be filtered out.

We do not know the exact token chunk length. Unless OpenAI publishes the details, this part is still guesswork. But the chunks are probably not very long.

Then comes the citation decision. Some links are cited, while many others are left out.

Some public analysis suggests that only a minority of retrieved pages end up appearing in the final answer as citations. I have seen the “around 15%” figure mentioned, but again, I would treat it as an estimate unless the source is clearly verified. The important point is that many pages may be retrieved and evaluated, then never shown to the user.

The key factor seems to be how well a page’s title and URL semantically match the subqueries. That means the smaller questions created during query planning, not necessarily the user’s original sentence.

This filtering may start before the page body is fully parsed.

Pages with clear structure and direct answers seem more likely to be selected than pages filled with vague terminology. Within the same candidate set, more mature and stable pages may also have an advantage. Simply being “newly published” does not seem to be enough.

Domain authority seems to matter more at the “getting into the candidate pool” stage than at the final citation stage. Once a page is already in the candidate set, the actual citation-rate gap between medium-authority and high-authority sites may not be as large as many people assume.