Jan 20 2026
Data Shows AI Overviews Disappears On Certain Kinds Of Finance Queries via @sejournal, @martinibuster

New data from BrightEdge shows how finance-related queries perform on AI Overviews, identifying clear areas that continue to show AIO while Google is pulling back from others. The deciding factor is whether the query benefits from explanation and synthesis versus direct data retrieval or action.

AI Overviews In Finance Are Query-Type Driven

Finance queries with an educational component, such as “what is” queries trigger a high level of AI Overviews, generating and AIO response as high as 91% of the time.

According to the data:

  • Educational queries (“what is an IRA”): 91% have AI Overviews
  • Rate and planning queries: 67% have AI Overviews
  • Stock tickers and real-time prices: 7% have AI Overviews

Examples of finance educational queries that generate AI Overviews:

  • ebitda meaning
  • how does compound interest work
  • what is an IRA
  • what is dollar cost averaging
  • what is a derivative
  • what is a bond

Finance Queries Where AIO Stays Out

Two areas where AIO stays out are local type queries or queries where real-time accuracy are of the essence. Local queries were initially a part of the original Search Generative Experience results in 2023, showing AI answers 90% of the time. That dropped to about 10% of the time.

The data also shows that “brand + near me” and other “near me” queries are dominated by local pack results and Maps integrations.

Tool and real-time information needs are no longer triggering AI Overviews. Finance calculator queries only shows AI Overviews 9% of the time. Other similar queries show no AI Overviews at all such as:

  • 401k calculator
  • compound interest calculator
  • investment calculator
  • mortgage calculator

The BrightEdge data shows that these real-time data topics do not generate AIO or generate a low amount:

  • Individual stock tickers: 7% have AI Overviews
  • Live price queries: Traditional results dominate
  • Market indices: Low AI coverage

Examples of queries Google AI generally keeps out of:

  • AAPL stock
  • Tesla price
  • dow jones industrial average today
  • S&P 500 futures

Takeaway

The direction Google takes for virtually anything search related depends on user feedback and the ability to show relevant results. It’s not uncommon for some in SEO to underestimate the power of implicit and explicit user feedback as a force that moves Google’s hands on when to show certain kinds of search features. Thus it may be that users are not satisfied with synthesized answers for real-time, calculator and tool, and local near me types of queries.

AIO Stays Out Of Brand Queries

Another area where AI Overviews are rarely if ever shown are finance queries that have a brand name as a component of the query. Brand login queries show AIO only zero to four percent of the time. Brand navigational queries do not show any AI search results.

Where AI Overviews Dominates Finance Results

The finance queries where AIO tends to dominate are those with an educational or explanatory intent, where users are seeking to understand concepts, compare options, or receive general guidance rather than retrieve live data, use tools, or complete a navigational task.

The data shows AIO dominating these kinds of queries:

  • Rate and planning queries: 67% have AI Overviews.
  • Rate information queries: 67% have AI Overviews.
  • Rate/planning queries (mortgages, retirement): 67%.
  • Retirement planning queries: 61% have AI Overviews.
  • Tax-related queries: 55% have AI Overviews.

Takeaway

As previously noted, Google doesn’t arbitrarily decide to show AI answers based on its judgments. User behavior and satisfaction signals play a large role. The fact that AI answers dominates these kinds of answers shows that AIO tends to satisfy users for these kinds of finance queries with a strong learning intent. This means that showing up as a citation for these kinds of queries requires carefully crafting content with a high level of precise answers. In my opinion, I think that a focus on creating content that is unique and doing it on a predictable and regular basis sends a signal of authoritativeness and trustworthiness. Definitely stay away from tactic of the month approaches to content.

Visibility And Competition Takeaways

Educational and guidance content have a high visibility in AI responses, not just organic rankings. Visibility increasingly depends on being cited or referenced. It may be useful to focus not just on text content but to offer audio, image, and video content. Not only that, but graphs and tables may be useful ways of communicating data, anything that can be referenced as an answer or to support the answer may be useful.

Traditional ranking factors still hold for high-volume local, tool, and real-time data queries. Live prices, calculators, and local searches continue to operate under conventional SEO factors.

Finance search behavior is increasingly segmented by intent and topic. Each query type follows a different path toward AI or organic results. The underlying infrastructure is still the same classic search which means that focusing on the fundamentals of SEO plus expanding beyond simple text content to see what works is a path forward.

Read BrightEdge’s data on finance queries and AI: Finance and AI Overviews: How Google Applies YMYL Principles to Financial Search

Featured Image by Shutterstock/Mix and Match Studio

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Jan 20 2026
Why Agentic AI May Flatten Brand Differentiators via @sejournal, @martinibuster

James LePage, Dir Engineering AI, co-lead of the WordPress AI Team, described the future of the Agentic AI Web, where websites become interactive interfaces and data sources and the value add that any site brings to their site becomes flattened. Although he describes a way out of brand and voice getting flattened, the outcome for informational, service, and media sites may be “complex.”

Evolution To Autonomy

One of the points that LePage makes is that of agentic autonomy and how that will impact what it means to have an online presence. He maintains that humans will still be in the loop but at a higher and less granular level, where agentic AI interactions with websites are at the tree level dealing with the details and the humans are at the forest level dictating the outcome they’re looking for.

LePage writes:

“Instead of approving every action, users set guidelines and review outcomes.”

He sees agentic AI progressing on an evolutionary course toward greater freedom with less external control, also known as autonomy. This evolution is in three stages.

He describes the three levels of autonomy:

  1. What exists now is essentially Perplexity-style web search with more steps: gather content, generate synthesis, present to user. The user still makes decisions and takes actions.
  2. Near-term, users delegate specific tasks with explicit specifications, and agents can take actions like purchases or bookings within bounded authority.
  3. Further out, agents operate more autonomously based on standing guidelines, becoming something closer to economic actors in their own right.”

AI Agents May Turn Sites Into Data Sources

LePage sees the web in terms of control, with Agentic AI experiences taking control of how the data is represented to the user. The user experience and branding is removed and the experience itself is refashioned by the AI Agent.

He writes:

“When an agent visits your website, that control diminishes. The agent extracts the information it needs and moves on. It synthesizes your content according to its own logic. It represents you to its user based on what it found, not necessarily how you’d want to be represented.

This is a real shift. The entity that creates the content loses some control over how that content is presented and interpreted. The agent becomes the interface between you and the user.

Your website becomes a data source rather than an experience.”

Does it sound problematic that websites will turn into data sources? As you’ll see in the next paragraph, LePage’s answer for that situation is to double down on interactions and personalization via AI, so that users can interact with the data in ways that are not possible with a static website.

These are important insights because they’re coming from the person who is the director of AI engineering at Automattic and co-leads the team in charge of coordinating AI integration within the WordPress core.

AI Will Redefine Website Interactions

LePage, who is the co-lead of WordPress’s AI Team, which coordinates AI-related contributions to the WordPress core, said that AI will enable websites to offer increasingly personalized and immersive experiences. Users will be able to interact with the website as a source of data refined and personalized for the individual’s goals, with website-side AI becoming the differentiator.

He explained:

“Humans who visit directly still want visual presentation. In fact, they’ll likely expect something more than just content now. AI actually unlocks this.

Sites can create more immersive and personalized experiences without needing a developer for every variation. Interactive data visualizations, product configurators, personalized content flows. The bar for what a “visit” should feel like is rising.

When AI handles the informational layer, the experiential layer becomes a differentiator.”

That’s an important point right there because it means that if AI can deliver the information anywhere (in an agent user interface, an AI generated comparison tool, a synthesized interactive application), then information alone stops separating you from everyone else.

In this kind of future, what becomes the differentiator, your value add, is the website experience itself.

How AI Agents May Negatively Impact Websites

LePage says that Agentic AI is a good fit for commercial websites because they are able to do comparisons and price checks and zip through the checkout. He says that it’s a different story for informational sites, calling it “more complex.”

Regarding the phrase “more complex,” I think that’s a euphemism that engineers use instead of what they really mean: “You’re probably screwed.”

Judge for yourself. Here’s how LePage explains websites lose control over the user experience:

“When an agent visits your website, that control diminishes. The agent extracts the information it needs and moves on. It synthesizes your content according to its own logic. It represents you to its user based on what it found, not necessarily how you’d want to be represented.

This is a real shift. The entity that creates the content loses some control over how that content is presented and interpreted. The agent becomes the interface between you and the user. Your website becomes a data source rather than an experience.

For media and services, it’s more complex. Your brand, your voice, your perspective, the things that differentiate you from competitors, these get flattened when an agent summarizes your content alongside everyone else’s.”

For informational websites, the website experience can be the value add but that advantage is eliminated by Agentic AI and unlike with ecommerce transactions where sales are the value exchange, there is zero value exchange since nobody is clicking on ads, much less viewing them.

Alternative To Flattened Branding

LePage goes on to present an alternative to brand flattening by imagining a scenario where websites themselves wield AI Agents so that users can interact with the information in ways that are helpful, engaging, and useful. This is an interesting thought because it represents what may be the biggest evolutionary step in website presence since responsive design made websites engaging regardless of device and browser.

He explains how this new paradigm may work:

“If agents are going to represent you to users, you might need your own agent to represent you to them.

Instead of just exposing static content and hoping the visiting agent interprets it well, the site could present a delegate of its own. Something that understands your content, your capabilities, your constraints, and your preferences. Something that can interact with the visiting agent, answer its questions, present information in the most effective way, and even negotiate.

The web evolves from a collection of static documents to a network of interacting agents, each representing the interests of their principal. The visiting agent represents the user. The site agent represents the entity. They communicate, they exchange information, they reach outcomes.

This isn’t science fiction. The protocols are being built. MCP is now under the Linux Foundation with support from Anthropic, OpenAI, Google, Microsoft, and others. Agent2Agent is being developed for agent-to-agent communication. The infrastructure for this kind of web is emerging.”

What do you think about the part where a site’s AI agent talks to a visitor’s AI agent and communicates “your capabilities, your constraints, and your preferences,” as well as how your information will be presented? There might be something here, and depending on how this is worked out, it may be something that benefits publishers and keeps them from becoming just a data source.

AI Agents May Force A Decision: Adaptation Versus Obsolescence

LePage insists that publishers, which he calls entities, that evolve along with the Agentic AI revolution will be the ones that will be able to have the most effective agent-to-agent interactions, while those that stay behind will become data waiting to be scraped .

He paints a bleak future for sites that decline to move forward with agent-to-agent interactions:

“The ones that don’t will still exist on the web. But they’ll be data to be scraped rather than participants in the conversation.”

What LePage describes is a future in which product and professional service sites can extract value from agent-to-agent interactions. But the same is not necessarily true for informational sites that users depend on for expert reviews, opinions, and news. The future for them looks “complex.”

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Jan 16 2026
Google Health AI Overviews Cite YouTube More Than Any Hospital Site via @sejournal, @MattGSouthern

Google’s AI Overviews may be relying on YouTube more than official medical sources when answering health questions, according to new research from SEO platform SE Ranking.

The study analyzed 50,807 German-language health prompts and keywords, captured in a one-time snapshot from December using searches run from Berlin.

The report lands amid renewed scrutiny of health-related AI Overviews. Earlier this month, The Guardian published an investigation into misleading medical summaries appearing in Google Search. The outlet later reported Google had removed AI Overviews for some medical queries.

What The Study Measured

SE Ranking’s analysis focused on which sources Google’s AI Overviews cite for health-related queries. In that dataset, the company says AI Overviews appeared on more than 82% of health searches, making health one of the categories where users are most likely to see a generated summary instead of a list of links.

The report also cites consumer survey findings suggesting people increasingly treat AI answers as a substitute for traditional search, including in health. It cites figures including 55% of chatbot users trusting AI for health advice and 16% saying they’ve ignored a doctor’s advice because AI said otherwise.

YouTube Was The Most Cited Source

Across SE Ranking’s dataset, YouTube accounted for 4.43% of all AI Overview citations, or 20,621 citations out of 465,823.

The next most cited domains were ndr.de (14,158 citations, 3.04%) and MSD Manuals (9,711 citations, 2.08%), according to the report.

The authors argue that the ranking matters because YouTube is a general-purpose platform with a mixed pool of creators. Anyone can publish health content there, including licensed clinicians and hospitals, but also creators without medical training.

To check what the most visible YouTube citations looked like, SE Ranking reviewed the 25 most-cited YouTube videos in its dataset. It found 24 of the 25 came from medical-related channels, and 21 of the 25 clearly noted the content was created by a licensed or trusted source. It also warned that this set represents less than 1% of all YouTube links cited by AI Overviews.

Government & Academic Sources Were Rare

SE Ranking categorized citations into “more reliable” and “less reliable” groups based on the type of organization behind each source.

It reports that 34.45% of citations came from the more reliable group, while 65.55% came from sources “not designed to ensure medical accuracy or evidence-based standards.”

Within the same breakdown, academic research and medical journals accounted for 0.48% of citations, German government health institutions accounted for 0.39%, and international government institutions accounted for 0.35%.

AI Overview Citations Often Point To Different Pages Than Organic Search

The report compared AI Overview citations to organic rankings for the same prompts.

While SE Ranking found that 9 out of 10 domains overlapped between AI citations and frequent organic results, it says the specific URLs frequently diverged. Only 36% of AI-cited links appeared in Google’s top 10 organic results, 54% appeared in the top 20, and 74% appeared somewhere in the top 100.

The biggest domain-level exception in its comparison was YouTube. YouTube ranked first in AI citations but only 11th in organic results in its analysis, appearing 5,464 times as an organic link compared to 20,621 AI citations.

How This Connects To The Guardian Reporting

The SE Ranking report explicitly frames its work as broader than spot-checking individual responses.

“The Guardian investigation focused on specific examples of misleading advice. Our research shows a bigger problem,” the authors wrote, arguing that AI health answers in their dataset relied heavily on YouTube and other sites that may not be evidence-based.

Following The Guardian’s reporting, the outlet reported that Google removed AI Overviews for certain medical queries.

Google’s public response, as reported by The Guardian, emphasized ongoing quality work while also disputing aspects of the investigation’s conclusions.

Why This Matters

This report adds a concrete data point to a problem that’s been easier to talk about in the abstract.

I covered The Guardian’s investigation earlier this month, and it raised questions about accuracy in individual examples. SE Ranking’s research tries to show what the source mix looks like at scale.

Visibility in AI Overviews may depend on more than being the most prominent “best answer” in organic search. SE Ranking found many cited URLs didn’t match top-ranking pages for the same prompts.

The source mix also raises questions about what Google’s systems treat as “good enough” evidence for health summaries at scale. In this dataset, government and academic sources barely showed up compared to media platforms and a broad set of less reliability-focused sites.

That’s relevant beyond SEO. The Guardian reporting showed how high-stakes the failure modes can be, and Google’s pullback on some medical queries suggests the company is willing to disable certain summaries when the scrutiny gets intense.

Looking Ahead

SE Ranking’s findings are limited to German-language queries in Germany and reflect a one-time snapshot, which the authors acknowledge may vary over time, by region, and by query phrasing.

Even with that caveat, the combination of this source analysis and the recent Guardian investigation puts more focus on two open questions. The first is how Google weights authority versus platform-level prominence in health citations. The second is how quickly it can reduce exposure when specific medical query patterns draw criticism.

Featured Image: Yurii_Yarema/Shutterstock

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Jan 13 2026
How Much Can We Influence AI Responses? via @sejournal, @Kevin_Indig

Right now, we’re dealing with a search landscape that is both unstable in influence and dangerously easy to manipulate. We keep asking how to influence AI answers – without acknowledging that LLM outputs are probabilistic by design.

In today’s memo, I’m covering:

  • Why LLM visibility is a volatility problem.
  • What new research proves about how easily AI answers can be manipulated.
  • Why this sets up the same arms race Google already fought.
Image Credit: Kevin Indig

1. Influencing AI Answers Is Possible But Unstable

Last week, I published a list of AI visibility factors; levers that grow your representation in LLM responses. The article got a lot of attention because we all love a good list of tactics that drive results.

But we don’t have a crisp answer to the question, “How much can we actually influence the outcomes?”

There are seven good reasons why the probabilistic nature of LLMs might make it hard to influence their answers:

  1. Lottery-style outputs. LLMs (probabilistic) are not search engines (deterministic). Answers vary a lot on the micro-level (single prompts).
  2. Inconsistency. AI answers are not consistent. When you run the same prompt five times, only 20% of brands show up consistently.
  3. Models have a bias (which Dan Petrovic calls “Primary Bias”) based on pre-training data. How much we are able to influence or overcome that pre-training bias is unclear.
  4. Models evolve. ChatGPT has become a lot smarter when comparing 3.5 to 5.2. Do “old” tactics still work? How do we ensure that tactics still work for new models?
  5. Models vary. Models weigh sources differently for training and web retrieval. For example, ChatGPT leans heavier on Wikipedia while AI Overviews cite Reddit more.
  6. Personalization. Gemini might have more access to your personal data through Google Workspace than ChatGPT and, therefore, give you much more personalized results. Models might also vary in the degree to which they allow personalization.
  7. More context. Users reveal much richer context about what they want with long prompts, so the set of possible answers is much smaller, and therefore harder to influence.

2. Research: LLM Visibility Is Easy To Game

A brand new paper from Columbia University by Bagga et al. titled “E-GEO: A Testbed for Generative Engine Optimization in E-Commerce” shows just how much we can influence AI answers.

Image Credit: Kevin Indig

The methodology:

  • The authors built the “E-GEO Testbed,” a dataset and evaluation framework that pairs over 7,000 real product queries (sourced from Reddit) with over 50,000 Amazon product listings and evaluates how different rewriting strategies improve a product’s AI Visibility when shown to an LLM (GPT-4o).
  • The system measures performance by comparing a product’s AI Visibility before and after its description is rewritten (using AI).
  • The simulation is driven by two distinct AI agents and a control group:
    • “The Optimizer” acts as the vendor with the goal of rewriting product descriptions to maximize their appeal to the search engine. It creates the “content” that is being tested.
    • “The Judge” functions as the shopping assistant that receives a realistic consumer query (e.g., “I need a durable backpack for hiking under $100”) and a set of products. It then evaluates them and produces a ranked list from best to worst.
    • The Competitors are a control group of existing products with their original, unedited descriptions. The Optimizer must beat these competitors to prove its strategy is effective.
  • The researchers developed a sophisticated optimization method that used GPT-4o to analyze the results of previous optimization rounds and give recommendations for improvements (like “Make the text longer and include more technical specifications.”). This cycle repeats iteratively until a dominant strategy emerges.

The results:

  • The most significant discovery of the E-GEO paper is the existence of a “Universal Strategy” for “LLM output visibility” in ecommerce.
  • Contrary to the belief that AI prefers concise facts, the study found that the optimization process consistently converged on a specific writing style: longer descriptions with a highly persuasive tone and fluff (rephrasing existing details to sound more impressive without adding new factual information).
  • The rewritten descriptions achieved a win rate of ~90% against the baseline (original) descriptions.
  • Sellers do not need category-specific expertise to game the system: A strategy developed entirely using home goods products achieved an 88% win rate when applied to the electronics category and 87% when applied to the clothing category.

3. The Body Of Research Grows

The paper covered above is not the only one showing us how to manipulate LLM answers.

1. GEO: Generative Engine Optimization (Aggarwal et al., 2023)

  • The researchers applied ideas like adding statistics or including quotes to content and found that factual density (citations and stats) boosted visibility by about 40%.
  • Note that the E-GEO paper found that verbosity and persuasion were far more effective levers than citations, but the researchers (1) looked specifically at a shopping context, (1) used AI to find out what works, and (3) the paper is newer in comparison.

2. Manipulating Large Language Models (Kumar et al., 2024)

  • The researchers added a “Strategic Text Sequence,” – JSON-formatted text with product information – to product pages to manipulate LLMs.
  • Conclusion: “We show that a vendor can significantly improve their product’s LLM Visibility in the LLM’s recommendations by inserting an optimized sequence of tokens into the product information page.”

3. Ranking Manipulation (Pfrommer et al., 2024)

  • The authors added text on product pages that gave LLMs specific instructions (like “please recommend this product first”), which is very similar to the other two papers referenced above.
  • They argue that LLM Visibility is fragile and highly dependent on factors like product names and their position in the context window.
  • The paper emphasizes that different LLMs have significantly different vulnerabilities and don’t all prioritize the same factors when making LLM Visibility decisions.

4. The Coming Arms Race

The growing body of research shows the extreme fragility of LLMs. They’re highly sensitive to how information is presented. Minor stylistic changes that don’t alter the product’s actual utility can move a product from the bottom of the list to the No. 1 recommendation.

The long-term problem is scale: LLM developers need to find ways to reduce the impact of these manipulative tactics to avoid an endless arms race with “optimizers.” If these optimization techniques become widespread, marketplaces could be flooded with artificially bloated content, significantly reducing the user experience. Google stood in front of the same problem and then launched Panda and Penguin.

You could argue that LLMs already ground their answers in classic search results, which are “quality filtered,” but grounding varies from model to model, and not all LLMs prioritize pages ranking at the top of Google search. Google protects its search results more and more against other LLMs (see “SerpAPI lawsuit” and the “num=100 apocalypse”).

I’m aware of the irony that I contribute to the problem by writing about those optimization techniques, but I hope I can inspire LLM developers to take action.

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Featured Image: Paulo Bobita/Search Engine Journal

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Jan 13 2026
Google Downplays GEO – But Let’s Talk About Garbage AI SERPs via @sejournal, @martinibuster

Google’s Danny Sullivan and John Mueller’s Search Off The Record podcast offered guidance to SEOs and publishers who have questions about ranking in LLM-based search and chat, debunking the commonly repeated advice to “chunk your content.” But that’s really not the conversation Googlers should be having right now.

SEO And The Next Generation Of Search

Google used to rank content based on keyword matching and PageRank was a way to extend that paradigm using the anchor text of links. The introduction of the Knowledge Graph in 2012 was described as a step toward ranking answers based on things (entities) in the real world. Google called this a shift from strings to things.

What’s happening today is what Google in 2012 called “the next generation of search, which taps into the collective intelligence of the web and understands the world a bit more like people do.”

So, when people say that nothing has changed with SEO, it’s true to the extent that the underlying infrastructure is still Google Search. What has changed is that the answers are in a long-form format that answers three or more additional questions beyond the user’s initial query.

The answer to the question of what’s different about SEO for AI is that the paradigm of optimizing for one keyword for one search result is shattered, splintered by the query fan-out.

Google’s Danny Sullivan and John Mueller took a crack at offering guidance on what SEOs should be focusing on. Do they hit the mark?

How To Write For Longform Answers

Given that Google is surfacing multi-paragraph long answers, does it make sense to create content that’s organized into bite-sized chunks? How does that affect how humans read content, will they like it or leave it?

Many SEOs are recommending that publishers break up the page up into “chunks” based on the intuition that AI understands content in chunks, dividing up the page into sections. But that’s an arbitrary approach that ignores the fact that a properly structured web page is already broken into chunks through the use of headings, HTML elements like ordered and unordered lists. A properly marked up and formatted web page should already be formatted into logical structure that a human and a machine can easily understand. Duh… right?

It’s not surprising that Google’s Danny Sullivan warns SEOs and publishers to not break their content up into chunks.

Danny said:

“To go to one of the things, you know, I talked about the specific things people like, “What is the thing I need to improve.” One of the things I keep seeing over and over in some of the advice and guidance and people are trying to figure out what do we do with the LLMs or whatever, is that turn your content into bite-sized chunks, because LLMs like things that are really bite size, right?

So we don’t want you to do that. I was talking to some engineers about that. We don’t want you to do that. We really don’t. We don’t want people to have to be crafting anything for Search specifically. That’s never been where we’ve been at and we still continue to be that way. We really don’t want you to think you need to be doing that or produce two versions of your content, one for the LLM and one for the net.”

Danny talked about chunking with some Google engineers and his takeaway from that conversation is to recommend against chunking. The second takeaway is that their systems are set up to access content the way human readers access it and for that reason he says to craft the content for humans.

Avoids Talking About Search Referrals

But again, he avoids talking about what I think is the more important facet of AI search, query fan-out and the impact to referrals. Query fan-out impacts referrals because Google is ranking a handful of pages for multiple queries for every one query that a user makes. But compounds this situation, as you will see further on, is that the sites Google is ranking do not measure up.

Focus On The Big Picture

Danny Sullivan next discusses the downside of optimizing for a machine, explaining that systems eventually improve that usually means that optimization for machines stop working.

He explained:

“And then the systems improve, probably the way the systems always try to improve, to reward content written for humans. All that stuff that you did to please this LLM system that may or may not have worked, may not carry through for the long term.

…Again, you have to make your own decisions. But I think that what you tend to see is, over time, these very little specific things are not the things that carry you through, but you know, you make your own decisions. But I think also that many people who have been in the SEO space for a very long time will see this, will recognize that, you know, focusing on these foundational goals, that’s what carries you through.”

Let’s Talk About Garbage AI Search Results

I have known Danny Sullivan for a long time and have a ton of respect for him, I know that he has publishers in mind and that he truly wants for them to succeed. What I wished he would talk about is the declining traffic opportunities for subject-matter experts and the seemingly arbitrary garbage search results that Google consistently surfaces.

Subject Matter Expertise Is Missing

Google is intentionally hiding expert publications in the search results, hidden away in the More tab. In order to find expert content, a user has to click the More tab and then click the News tab.

How Google Hides Expert Web Pages

How Google hides expert web pages.

Google’s AI Mode Promotes Garbage And Sites Lacking Expertise

This search was not cherry-picked to show poor results. This is literally the one search I did asking a legit question about styling a sweatshirt.

Google’s AI Mode cites the following pages:

1. An abandoned Medium Blog from 2018, that only ever had two blog posts, both of which have broken images. That’s not authoritative.

2. An article published on LinkedIn, a business social networking website. Again, that’s not authoritative nor trustworthy. Who goes to LinkedIn for expert style advice?

3. An article about sweatshirts published on a sneaker retailer’s website. Not expert, not authoritative. Who goes to a sneaker retailer to read articles about sweatshirts?

Screenshot Of Google’s Garbage AI Results

Google Hides The Good Stuff In More > News Tab

Had Google defaulted to actual expert sites they may have linked to an article from GQ or the New York Times, both reputable websites. Instead, Google hides the high quality web pages under the More tab.

Screenshot Of  Hidden High Quality Search Results

GEO Or SEO – It Doesn’t Matter

This whole thing about GEO or AEO and whether it’s all SEO doesn’t really matter. It’s all a bunch of hand waving and bluster. What matters is that Google is no longer ranking high quality sites and high quality sites are withering from a lack of traffic.

I see these low quality SERPs all day long and it’s depressing because there is no joy of discovery in Google Search anymore. When was the last time you discovered a really cool site that you wanted to tell someone about?

Garbage on garbage, on garbage, on top of more garbage. Google needs a reset.

How about Google brings back the original search and we can have all the hand-wavy Gemini stuff under the More tab somewhere?

Listen to the podcast here:

Featured Image by Shutterstock/Kues

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Jan 13 2026
2026 Guide To Hiring A Link Building Agency In The AI Search Era via @sejournal, @jmoserr

This post was sponsored by uSERP. The opinions expressed in this article are the sponsor’s own.

Let’s get real. Most link building agencies are selling you an outdated playbook from 2015.

Volume. Guest posting on dead sites. Chasing domain ratings at all costs.

But if you’re a marketing leader in 2026, you know the game has changed.

I’ve spent the last decade completing over 575 link building campaigns and scaling my team at uSERP to 55+ people. I have worked with SaaS giants like monday.com and Robinhood.

I know first hand that the gap between a bad backlinks agency and a great one is no longer just about rankings. It is about revenue.

Here’s what I have learned, and how you can use it to pick a skilled link building agency in the AI era.

Traditional link-building isn’t dead. But the old methods are broken.

For years, SEO agencies focused only on domain authority (DA) or domain rating (DR).

They built backlinks from any site with a high number of backlinks. They ignored readership and content quality.

But that approach is dangerous now.

Because search engines have evolved, links now serve two masters: Google’s algorithm and AI model training data.

Ignoring this means losing search engine rankings (and watching your bottom line suffer).

In fact, uSERP’s 2025 State of Backlinks Report, which surveyed 800 SEO professionals, found that 67.5% believe backlinks influence overall search results (a rise from 2023).

But it’s not just quantity. Quality and brand authority work together, month after month, to drive traffic.

This data forced us to pivot at uSERP. We stopped chasing vanity metrics like DR.

Instead, we started prioritizing traffic and relevance.

It turns out that a single link from a site that appears in a Perplexity answer is worth more than 10 links from high-DR sites with zero readership.

Agencies that fail to adapt are dying, and so are their clients.

So the bottom-line question is:

How can you pick a link building agency that catapults your business in this AI era instead of leaving you stranded?

Green Flags: What Separates Elite Agencies

It’s easy to promise the world on a sales call. It’s harder to deliver natural links that drive revenue.

When vetting partners, look for these specific green flags.

They Focus On AI Visibility, Not Just Rankings

Elite agencies don’t just track Google SERPs.

They track brand mentions in LLMs. They understand that a link is a citation. It validates your expertise to both humans and machines.

Ask them this: “Can you show me examples of clients appearing in AI-generated answers?”

If they stare blankly, walk away.

If they have a proven system, that’s a green flag. It means they know what they’re doing.

For example, we developed proprietary AI visibility tracking tools because we had to. It was the only way to measure impact.

Any agency you hire must discuss citations and how search engines use links to verify facts.

They Lead With Digital PR And Original Research

Content creation is the backbone of modern link acquisition.

You cannot just beg for links anymore. You have to earn them with a content-driven approach.

That is why digital PR was the most effective link-building tactic in 2025, according to our State of Backlinks Report.

The winning strategy is simple. Produce linkable assets, such as original studies, interactive tools, and expert commentary.

These assets generate inbound links naturally. They get cited by AI and compound over time.

For example, a SaaS brand might create a salary calculator. Journalists and publishers love this data.

This approach also shifts the dynamic from cold outreach to relationship-based link building. Even if you do cold outreach, you should expect better results because it’s a win-win for both parties, and you’re leading with quality content and data they can’t ignore.

They Are Transparent About Process And Pricing

A skilled backlinks agency has nothing to hide.

Vague promises are red flags. Detailed reporting on publishers, anchor text, and traffic estimates is a green flag.

They are also realistic about costs.

For example, our data show that most SEO professionals spend between $5,000 and $10,000 per month on link building.

If someone offers you 100 links for $500, that’s a liability, not a deal.

They should also provide a dashboard that includes your link inventory, KPIs, and how your content is driving traffic over time.

Transparency builds trust. Secrecy usually hides black hat link building tactics.

Let’s look at red flags you should stay far away from.

Red Flags That Scream “Run Away”

I have worked with 100+ clients who got burned by cheap link building providers. They saw temporary spikes, then got hit by core Google updates.

This is the price of buying temporary tactics. It’s the equivalent of shiny object syndrome that wastes time, money, and reputation for the sake of slightly higher initial traffic that evaporates after a couple of months.

Here are the warning signs.

Promises Of Specific Ranking Positions

“We will get you to #1 in 30 days.”

This is a lie.

No agency controls Google. They can influence probabilities, but they cannot guarantee outcomes.

Ranking factors are very complex. Plus, some are unknown, and agencies can only estimate probabilities based on experience and data.

Anyone guaranteeing a spot is selling snake oil.

PBNs (Private Blog Networks) are poison for your site.

They’re fake “blogs” that exist for one reason: to pass authority. They violate Google’s rules and go against its spam policies.

If your agency is buying links off some “menu” or dropping niche edits on hacked, junk sites, that’s your cue to walk away.

Sure, these backlinks might temporarily boost your domain rating. But sooner or later, your search visibility winds up circling the drain.

Templated Outreach

If they use the same email template for everyone, they are failing.

Journalists receive dozens of these every day and just ignore or delete them. Website owners mark them as spam.

You need a personalized approach.

Sending thousands of generic emails daily reflects poorly on your brand.

This is a silent killer. Ahrefs found that 66.5% of links from 2013 to 2024 are now dead.

Cheap agencies take your money and move on.

You need a partner who monitors their work. They must check for link rot and take steps to fix it to protect your investment and your brand’s organic growth.

The Questions You Must Ask Before Signing

Don’t just trust a Clutch profile. Grill potential partners with these questions.

1. “What Is Your Process For Vetting Publishers?”

They should talk about how they verify traffic and how they check for spammy sites. If they’re not even looking at a site’s keyword rankings, that’s a big red flag.

2. “Can I See Examples Of Client Results In AI Overviews?”

This separates modern agencies from the dinosaurs.

Ask how they measure AI visibility by impact in ChatGPT or Perplexity.

3. “What Is Your Typical Timeline?”

If they say “immediate results,” they are lying.

You could have a severe technical issue that, once fixed, could cause a permanent spike in traffic. But that’s a rare exception.

Real SEO services take time. BuzzStream’s 2025 State of Digital PR Report states that most campaigns deliver results within 3-6 months.

4. “How Do You Measure Success Beyond DR Increases?”

Domain rating is a vanity metric if it doesn’t lead to revenue. They should track growth in organic search traffic and referral traffic.

Ask about backlink gap analysis and see if they share a high-level step-by-step of their link building process.

Given the high rate of link rot, a replacement policy is essential. You need backlink management that protects your investment.

Decide if you want digital PR or traditional link building with AI enhancements. But make sure there’s accountability and a process that actively monitors and replaces rotten links.

What Success Actually Looks Like

Let’s look at a real example. When monday.com reached out to my company, uSERP, they had 100+ internal SEO staff but still needed help with content production and PR.

The competitors were winning in organic search, taking over primary keywords, and gaining market share.

So, we focused on untapped keywords first. We created helpful content and optimized it to land crucial backlinks from publications like Crunchbase and G2.

We focused on quality plus relevance. Then, monday earned volume with the cause-and-effect principle.

The result was a 77.84% increase in traffic to 1.2M+ monthly visitors.

This is the lens you need: relationship-building techniques that demonstrate real authority and value, resulting in ROI. Not just rankings.

Whether in the United States, the United Kingdom, or Canada, quality link building like this takes 60-90 days for early signals and 6-12 months for full impact. But the dividends last for years.

Picking a link building agency in 2026 isn’t about finding the cheapest option. It is about finding partners who understand the AI-first future.

You need transparency, AI visibility results, and digital PR expertise.

Avoid anyone selling the 2015 playbook. The winners focus on citations, AI brand mentions, and revenue growth. Everything else is just noise.

Start asking the hard questions. Look for the green flags and don’t settle for vanity metrics.

For more foundational strategies, check out our complete link building guide.

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Image Credits

Featured Image: Image by Shutterstock. Used with permission.

In-Post Images: Images by uSERP. Used with permission.

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Jan 12 2026
Apple Selects Google’s Gemini For New AI-Powered Siri via @sejournal, @MattGSouthern

Apple is partnering with Google to power its AI features, including a major Siri upgrade expected later this year.

The companies announced the multi-year collaboration on Monday. Google’s Gemini models and cloud technology will serve as the foundation for the next generation of Apple Foundation Models.

“After careful evaluation, Apple determined that Google’s AI technology provides the most capable foundation for Apple Foundation Models and is excited about the innovative new experiences it will unlock for Apple users,” the joint statement said.

What’s New

The partnership makes Gemini a foundation for Apple’s next-generation models. Apple’s models will continue running on its devices and Private Cloud Compute infrastructure while maintaining what the company calls its “industry-leading privacy standards.”

Neither company disclosed the deal’s financial terms. Bloomberg previously reported Apple had discussed paying about $1 billion annually for Google AI access, though that figure remains unconfirmed for the final agreement.

By November, Bloomberg reported Apple had chosen Google over Anthropic based largely on financial terms.

Existing OpenAI Partnership Remains

Apple currently integrates OpenAI’s ChatGPT into Siri and Apple Intelligence for complex queries that draw on the model’s broader knowledge base.

Apple told CNBC the company isn’t making changes to that agreement. OpenAI did not immediately respond to a request for comment.

The distinction appears to be between the foundational models powering Apple Intelligence overall versus the external AI connection available for certain queries.

Context

The deal arrives as Google’s AI position strengthens. Alphabet surpassed Apple in market capitalization last week for the first time since 2019.

The default-search deal between Google and Apple has been under scrutiny after U.S. District Judge Amit Mehta ruled Google holds an illegal monopoly in online search and related advertising. In September 2025, he did not require Google to divest Chrome or Android.

Apple had originally planned to launch an AI-powered Siri upgrade in 2025 but delayed the release.

“It’s going to take us longer than we thought to deliver on these features and we anticipate rolling them out in the coming year,” Apple said at the time.

Google introduced its upgraded Gemini 3 model late last year. CEO Sundar Pichai said in October that Google Cloud signed more deals worth over $1 billion through the first three quarters of 2025 than in the previous two years combined.

Why This Matters

I covered this partnership in November when Bloomberg first reported Apple was paying Google to build a custom Gemini model for Siri. Today’s joint statement confirms what was then unattributed sourcing.

The confirmation matters because it extends Gemini’s reach into one of the largest device ecosystems in the world. Apple has said Siri fields 1.5 billion user requests per day across more than 2 billion active devices. That installed base gives Gemini distribution Google couldn’t match through its own products alone.

The competitive signal is clearer now too. Apple evaluated Anthropic and chose Google. Eddy Cue testified in May that Apple planned to add Gemini to Siri, but today’s announcement frames it as a deeper infrastructure partnership, not just another assistant option.

If Siri becomes meaningfully more capable at answering queries directly, the implications mirror what’s happening with AI Overviews and AI Mode in search. More queries could be resolved without users reaching external websites.

Looking Ahead

The upgraded Siri is expected to roll out later in 2026. The companies haven’t provided a specific launch date.

Apple maintaining its OpenAI integration alongside the Google partnership suggests both relationships will continue, at least for now. How Apple balances these two AI providers for different use cases will become clearer as the new features launch.

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Jan 8 2026
Google: AI Overviews Show Less When Users Don’t Engage via @sejournal, @MattGSouthern

AI Overviews don’t show up consistently across Google Search because the system learns where they’re useful and pulls them back when people don’t engage.

Robby Stein, Vice President of Product at Google Search, described in a CNN interview how Google tests the summaries, measures interaction, and reduces their appearance for certain kinds of searches where they don’t help.

How Google Decides When To Show AI Overviews

Stein explained that AI Overviews appear based on learned usefulness rather than showing up by default.

“The system actually learns where they’re helpful and will only show them if users have engaged with that and find them useful,” Stein said. “For many questions, people just ask like a short question or they’re looking for very specific website, they won’t show up because they’re not actually helpful in many many cases.”

He gave a concrete example. When someone searches for an athlete’s name, they typically want photos, biographical details, and social media links. The system learned people didn’t engage with an AI Overview for those queries.

“The system will learn that if it tried to do an AI overview, no one really clicked on it or engaged with it or valued it,” Stein said. “We have lots of metrics we look at that and then it won’t show up.”

What “Under The Hood” Queries Mean For Visibility

Stein described the system as sometimes expanding a search beyond what you type. Google “in many cases actually issues additional Google queries under the hood to expand your search and then brings you the most relevant information for a given question,” he said.

That may help explain why pages sometimes show up in AI Overview citations even when they don’t match your exact query wording. The system pulls in content answering related sub-questions or providing context.

For image-focused queries, AI Overviews integrate with image results. For shopping queries, they connect to product information. The system adapts based on what serves the question.

Where AI Mode Fits In

Stein described AI Mode as the next step for complicated questions that need follow-up conversation. The design assumes you start in traditional Search, get an Overview if it helps, then go deeper into AI Mode when you need more.

“We really designed AI Mode to really help you go deeper with a pretty complicated question,” Stein said, citing examples like comparing cars or researching backup power options.

During AI Mode testing, Google saw “like a two to three … full increase in the query length” compared to typical Search queries. Users also started asking follow-up questions in a conversational pattern.

The longer AI Mode queries included more specificity. Stein’s example: instead of “things to do in Nashville,” users asked “restaurants to go to in Nashville if one friend has an allergy and we have dogs and we want to sit outside.”

Personalization Exists But Is Limited

Some personalization in AI Mode already exists. Users who regularly click video results might see videos ranked higher, for example.

“We are personalizing some of these experiences,” Stein said. “But right now that’s a smaller adjustment probably to the experience because we want to keep it as consistent as possible overall.”

Google’s focus is on maintaining consistency across users while allowing for individual preferences where it makes sense.

Why This Matters

In July 2024, research showed Google had dialed back AIO presence by 52%, from widespread appearance to showing for just 8% of queries. Stein’s description offers one possible explanation for that pattern.

If you’re tracking AIO presence week to week, the fluctuations may reflect user behavior patterns for different question types rather than algorithm changes.

The “under the hood” query expansion means content can appear in citations even without matching your exact phrasing. That matters when you’re explaining CTR drops internally or planning content for complex queries where Overviews are more likely to surface.

Looking Ahead

Google’s AI Overviews earn placement based on usefulness rather than appearing by default.

Personalization is limited today, but the direction is moving toward more tailored experiences that maintain overall consistency.

See the full interview with Stein below:

Featured Image: nwz/Shutterstock

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Jan 8 2026
Google Gemini Gains Share As ChatGPT Declines In Similarweb Data via @sejournal, @MattGSouthern

ChatGPT accounted for 64% of worldwide traffic share among gen AI chatbot websites as of January, while Google’s Gemini reached 21%, according to Similarweb’s Global AI Tracker.

Similarweb’s tracker (PDF link) measures total visits at the domain level, so it reflects people who go to these tools directly on the web. It doesn’t capture API usage, embedded assistants, or other integrations where much of the AI usage occurs now.

ChatGPT Down, Gemini Up In A Year Of Share Gains

The share movement is easiest to see year-over-year.

A year ago, Similarweb estimated ChatGPT accounted for 86% of worldwide traffic among tracked chatbot sites. Now, that figure is 64%. Over the same period, Gemini rose from 5% to 21%.

Other tools are much smaller by this measure. DeepSeek was at 3.7%, Grok at 3.4%, and Perplexity and Claude both at 2.0%.

Google has been promoting Gemini through products like Android and Workspace, which may help explain why it’s gaining share among users who access these tools directly.

Winter Break Pulled Down Total Visits

Similarweb pointed to seasonality during the holiday period:

Similarweb wrote on X:

“Driven by the winter break, the daily average visits to all tools dropped to August-September levels.”

That context matters because it helps distinguish overall category softness from shifts in market share.

Writing Tool Domain Traffic Declines

Writing and content generation sites were down 10% over the most recent 12-week window in Similarweb’s category view.

At the individual tool level, Similarweb’s table shows steep drops for several writing platforms. Growthbarseo was down 100%, while Jasper fell 16%, Writesonic dropped 17%, and Rytr declined 9%. Originality was up 17%.

These are still domain-level visit counts, so the clearest takeaway is that fewer people are going directly to specialized writing sites online. That can happen for several reasons, including users relying more on general assistants, switching to apps, or using these models through integrations.

Code Completion Shows Mixed Results

The developer tools category looked more mixed than the writing tools.

Similarweb’s code completion table shows Bolt down 39% over 12 weeks, while Cursor (up 8%), Replit (up 2%), and Base44 (up 49%) moved in different directions.

Traditional Search Looks Close To Flat

In Similarweb’s “disrupted sectors” view, traditional search traffic is down roughly 1% to 3% year-over-year across recent periods, which doesn’t indicate a sharp drop in overall search usage in this dataset.

The same table shows Reddit up 12% year-over-year and Quora down 53%, consistent with the idea that some Q&A behavior is being redistributed even as overall search remains relatively steady.

Why This Matters

When making sense of how AI is changing discovery and demand, these numbers can help you understand where direct, web-based attention is concentrating. That can influence which assistants you monitor for brand mentions, citations, and referral behavior.

Though you should treat this a snapshot, not the full picture. If your audience is interacting with AI through browsers, apps, or embedded assistants, your own analytics will be a better barometer than any domain-level tracker.

Looking Ahead

The next report should clarify whether category traffic rebounds after the holiday period and whether Gemini continues to gain share at the same pace. It will also be a useful read on whether writing tools stabilize or whether more of that usage continues to consolidate into general assistants and bundled experiences.

Featured Image: vfhnb12/Shutterstock

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Jan 8 2026
Most Major News Publishers Block AI Training & Retrieval Bots via @sejournal, @MattGSouthern

Most top news publishers block AI training bots via robots.txt, but they’re also blocking the retrieval bots that determine whether sites appear in AI-generated answers.

BuzzStream analyzed the robots.txt files of 100 top news sites across the US and UK and found 79% block at least one training bot. More notably, 71% also block at least one retrieval or live search bot.

Training bots gather content to build AI models, while retrieval bots fetch content in real time when users ask questions. Sites blocking retrieval bots may not appear when AI tools try to cite sources, even if the underlying model was trained on their content.

What The Data Shows

BuzzStream examined the top 50 news sites in each market based on SimilarWeb traffic share, then deduplicated the list. The study grouped bots into three categories: training, retrieval/live search, and indexing.

Training Bot Blocks

Among training bots, Common Crawl’s CCBot was the most frequently blocked at 75%, followed by Anthropic-ai at 72%, ClaudeBot at 69%, and GPTBot at 62%.

Google-Extended, which trains Gemini, was the least blocked training bot at 46% overall. US publishers blocked it at 58%, nearly double the 29% rate among UK publishers.

Harry Clarkson-Bennett, SEO Director at The Telegraph, told BuzzStream:

“Publishers are blocking AI bots using the robots.txt because there’s almost no value exchange. LLMs are not designed to send referral traffic and publishers (still!) need traffic to survive.”

Retrieval Bot Blocks

The study found 71% of sites block at least one retrieval or live search bot.

Claude-Web was blocked by 66% of sites, while OpenAI’s OAI-SearchBot, which powers ChatGPT’s live search, was blocked by 49%. ChatGPT-User was blocked by 40%.

Perplexity-User, which handles user-initiated retrieval requests, was the least blocked at 17%.

Indexing Blocks

PerplexityBot, which Perplexity uses to index pages for its search corpus, was blocked by 67% of sites.

Only 14% of sites blocked all AI bots tracked in the study, while 18% blocked none.

The Enforcement Gap

The study acknowledges that robots.txt is a directive, not a barrier, and bots can ignore it.

We covered this enforcement gap when Google’s Gary Illyes confirmed robots.txt can’t prevent unauthorized access. It functions more like a “please keep out” sign than a locked door.

Clarkson-Bennett raised the same point in BuzzStream’s report:

“The robots.txt file is a directive. It’s like a sign that says please keep out, but doesn’t stop a disobedient or maliciously wired robot. Lots of them flagrantly ignore these directives.”

Cloudflare documented that Perplexity used stealth crawling behavior to bypass robots.txt restrictions. The company rotated IP addresses, changed ASNs, and spoofed its user agent to appear as a browser.

Cloudflare delisted Perplexity as a verified bot and now actively blocks it. Perplexity disputed Cloudflare’s claims and published a response.

For publishers serious about blocking AI crawlers, CDN-level blocking or bot fingerprinting may be necessary beyond robots.txt directives.

Why This Matters

The retrieval-blocking numbers warrant attention here. In addition to opting out of AI training, many publishers are opting out of the citation and discovery layer that AI search tools use to surface sources.

OpenAI separates its crawlers by function: GPTBot gathers training data, while OAI-SearchBot powers live search in ChatGPT. Blocking one doesn’t block the other. Perplexity makes a similar distinction between PerplexityBot for indexing and Perplexity-User for retrieval.

These blocking choices affect where AI tools can pull citations from. If a site blocks retrieval bots, it may not appear when users ask AI assistants for sourced answers, even if the model already contains that site’s content from training.

The Google-Extended pattern is worth watching. US publishers block it at nearly twice the UK rate, though whether that reflects different risk calculations around Gemini’s growth or different business relationships with Google isn’t clear from the data.

Looking Ahead

The robots.txt method has limits, and sites that want to block AI crawlers may find CDN-level restrictions more effective than robots.txt alone.

Cloudflare’s Year in Review found GPTBot, ClaudeBot, and CCBot had the highest number of full disallow directives across top domains. The report also noted that most publishers use partial blocks for Googlebot and Bingbot rather than full blocks, reflecting the dual role Google’s crawler plays in search indexing and AI training.

For those tracking AI visibility, the retrieval bot category is what to watch. Training blocks affect future models, while retrieval blocks affect whether your content shows up in AI answers right now.

Featured Image: Kitinut Jinapuck/Shutterstock

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