SEO Archives SEO

Apr 30 2025

AI Search & SEO: Key Trends and Insights [Webinar] via @sejournal, @lorenbaker

As AI continues to reshape search, marketers and SEOs are facing a new set of challenges and opportunities.

From the rise of AI Overviews to shifting SERP priorities, it’s more important than ever to know what to focus on in 2025.

Why This Webinar Is a Must-Attend Event

In this session, you’ll get:

You’ll Learn How To:

Adapt your approach to optimize for both answer engines and traditional search engines.
Create top-of-SERP content that stands out to AI Overviews.
Update technical SEO strategies for the AI era.
Use success in conversions as the overall KPI.

Expert Insights From Conductor

Join Shannon Vize, Sr. Content Marketing Manager at Conductor, and Pat Reinhart, VP of Services & Thought Leadership, as they walk through the biggest search and content shifts shaping 2025. From Google’s AI Overviews to new content strategies that actually convert, you’ll get clear guidance to help you move forward with confidence.

Don’t Miss Out!

Join us live and walk away with a clear roadmap for leading your SEO and content strategy in 2025.

Can’t attend live?

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Apr 30 2025

SEO Rockstar Names 7 SEO Fundamentals To Win In AI Search via @sejournal, @martinibuster

Todd Friesen, one of the most experienced digital marketers in our industry, recently posted on LinkedIn that the core fundamentals that apply to traditional search engines work exactly the same for AI search optimization. His post quickly received dozens of comments and more than a hundred likes, indicating that he’s not the only one who believes there’s no need to give SEO another name.

Who Is Todd Friesen?

Todd has had a long career in SEO, formerly of Salesforce and other top agencies and businesses. Like me, he was a moderator at the old WebmasterWorld Forums, only he’s been doing SEO for even longer. Although he’s younger than I am, I totally consider him my elder in the SEO business. Todd Friesen, along with Greg Boser, was an SEO podcasting pioneer with their SEO Rockstars show.

AEO – Answer Engine Optimization

There’s been a race to give a name to optimizing web content for AI search engines and few details on why it merits a new name.

We find ourselves today with five names for the exact same thing:

AEO (Answer Engine Optimization)
AIO (AI Optimization)
CEO (Chat Engine Optimization)
GEO (Generative Engine Optimization)
LMO (Language Model Optimization)

There are many people today that agree that optimizing for an AI search engine is fundamentally the same as optimizing for a traditional search engine. There’s little case for a new name when even an AI search engine like Perplexity uses a version of Google’s PageRank algorithm for ranking authoritative websites.

Todd Friesen’s post on LinkedIn made the case that optimizing for AI search engines is essentially the same thing as SEO:

“It is basically fundamental SEO and fundamental brand building. Can we stop over complicating it?

– proper code (html, schema and all that)
– fast and responsive site
– good content
– keyword research (yes, we still do this)
– coordination with brand marketing
– build some links
– analytics and reporting (focus on converting traffic)
– rinse and repeat”

SEO For AI = The Same SEO Fundamentals

Todd Friesen is right. While there’s room for quibbling about the details the overall framework for SEO, regardless if it’s for an AI search engine or not, can be reduced to these seven fundamentals of optimization.

Digital Marketer Rosy Callejas (LinkedIn Profile) agreed that there were too many names for the same thing:

“Too many names! SEO vs AEO vs GEO”

Kevin Doory, (LinkedIn Profile) Director Of SEO at RazorFish commented:

“The ones that talk about what they do, can change the names to whatever they want. The rest of us will just do the darn things.”

SEO Consultant Don Rhoades (LinkedIn Profile) agreed:

“Still SEO after all these (failed) attempts to distance from it by “thought leaders” – eg: inbound marketing, growth hacking, and whatever other nomenclature du jour they decide to cook up next.”

Ryan Jones (LinkedIn Profile), Senior Vice President, SEO at Razorfish (and founder of SERPrecon.com) commented on the ridiculousness of the GEO name:

“GEO is a terrible name”

Pushback On AEO Elsewhere

A discussion on Bluesky saw Google’s John Mueller commenting on the motivations for creating hype.

Preeti Gupta‬ posted her opinion on Bluesky:

“It is absolutely wild to me that in this debate of GEO/AEO and SEO, everyone is saying that building a brand is not a requisite for SEO, but it is important for GEO/AEO.

Like bro, chill. This AI stuff didn’t invent the need for building a brand. It existed way before it. smh.”

Google’s John Mueller responded:

“You don’t build an audience online by being reasonable, and you don’t sell new things / services by saying the current status is sufficient.”

What Do You Think?

What’s your opinion? Is SEO for AI fundamentally the same as for regular search engines?

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Apr 30 2025

How to Track Visibility in GenAI Platforms

Keyword rank tracking was once an essential search engine optimization tactic. But consumers are increasingly searching on generative AI platforms, which do not disclose prompt data, such as words and phrases.

Moreover, genAI responses are highly dynamic and personalized. A site may appear in an answer to an initial prompt or, alternatively, in a follow-up.

How can brands evaluate visibility in AI-driven answers against competitors and adjust strategy accordingly?

There are no good answers.

Yet new software solutions are attempting to address the need in various ways.

Knowatoa

Knowatoa is an AI visibility analysis tool with two primary components:

Crawlability status is a rough equivalent of Search Console for various genAI bots. It checks whether AI bots can access and crawl your site (based on the robots.txt file and hosting settings).

Visibility analysis scrutinizes your presence in answers on ChatGPT, Claude, Meta AI, and Perplexity.

To use, create an account and enter your domain. The tool will pull keywords (from third-party providers such as Semrush) and use them to generate commercial intent prompts, those that could trigger product or company responses.

Users can then review the prompts and add or delete according to their marketing approach. Users then create a project to collect answers to the prompts from the AI platforms.

The ensuing report resembles a rank tracking tool, allowing you to see which responses include your brand and where. It also discloses the exact answers to a given prompt.

Knowatoa generates prompts from users’ keywords to see which responses include the users’ brands. Click image to enlarge.

The tool also provides a question analysis based on your keywords that includes intent, category, and stage, such as “awareness” or “consideration.”

Knowatoa is free to register and obtain an initial analysis. Paid plans start at $49 per month.

Screenshot of a Knowatoa question analysis.

Knowatoa’s question analysis includes intent, category, and stage, such as “awareness” or “consideration.” Click image to enlarge.

Essio

Essio is another premium AI visibility tool with a more generic and visual approach. It provides users with a visibility score across multiple AI platforms, but it doesn’t show which prompts produce brand mentions.

Essio provides users with a visibility score across multiple AI platforms. Click image to enlarge.

My favorite Essio feature is its listing of better-performing competitors, those included in answers for a user’s prompts.

The most actionable part of the report is “influential” links, i.e., various URLs included in responses to your most relevant prompts. The report is handy for reverse engineering the process — responses vs. prompts.

Essio’s pricing starts at $75 per month. To me, it suits large brands that seek a broad overview of their AI visibility.

Screenshot of a top source by search report, showing influential souces.

Essio’s report for “influential sources” is handy for reverse engineering prompts. Click image to enlarge.

Waikay

Waikay checks the training data of ChatGPT, Gemini, Perplexity, and Claude to ascertain what they know about your brand and competitors.

Waikay checks ChatGPT, Gemini, Perplexity, and Claude to ascertain what they know about your brand and competitors. Click image to enlarge.

Waikay identifies concepts your brand is associated with and tracks “knowledge gaps,” i.e., topics associated with your competitors but not your brand.

Users can rerun reports to see how the training data and missing concepts are evolving for their brands with the addition of new content. Users can create a report on any knowledge gap and receive content topics and ideas.

Waikay runs automated monthly reports to track how users’ content marketing efforts impact AI training data.

Waikay offers a “brand report” for free. Paid plans start at $19.95 per month.

Waikay’s AI Knowledge Map identifies topics associated with your competitors but not your brand. Click image to enlarge.

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Apr 29 2025

Google On Diluting SEO Impact Through Anchor Text Overuse via @sejournal, @martinibuster

Google’s John Mueller answered a question about internal site navigation where an SEO was concerned about diluting the ability to rank for a keyword phrase by using the same anchor text in four sitewide sections across an entire website.

Link In Four Navigational Areas

The person asking the question had a client that had four navigational areas with links across the entire site. One of the links is repeated across each of the four navigational areas, using the exact same anchor text. The concern is that using that phrase multiple times across the entire site might cause it to appear overused.

Roots of Why SEOs Worry About Anchor Text Overuse

There’s a longtime concern in the SEO industry about overusing anchor text. The original reason for this concern, the root of it, is because overusing internal anchor text could be seen as communicating the intent to manipulate the search engines. This concern arose in 2005 because of Google’s announced use of statistical analysis which can identify unnatural linking patterns.

Over the years that concern has evolved to worrying about “diluting” the impact of anchor text, which has no foundation in anything Google said although Google is on record as saying that they’re dampening sitewide links.

Google has in the past made it known that it divides a page into its constituent parts such as the header section (where the logo is), the sitewide navigation, sidebars, main content, in-content navigation, advertising and footers.

We know that Google has been doing this since at least 2004 (a Googler confirmed it to me at a search event) and most definitely around 2006-ish when Google was dampening the effect of external sitewide links and internal sitewide links so that the links only counted as one link, and not with the full power of 2,000 or whatever number of links.

Back in the day people were selling sitewide links at a premium because they were said to harness the entire PageRank power of the site. So Google announced that those links would be dampened for internal links and Google began recognizing paid links and blocking the PageRank from transferring.

We could see the power of the sitewide links through Google’s browser toolbar that contained a PageRank meter so when the change happened we were able to confirm that effect in the toolbar and in rankings.

That’s why sitewide links are no longer an SEO thing anymore. It’s has nothing to do with dilution.

Sitewide Links And Dilution 2025

Today we find an SEO who’s worrying about a sitewide anchor text link being “diluted.”

So, if we already know that Google recognizes sidebars, menus and footers and separates them out from the main content and we know that Google doesn’t count a sitewide link as a multiple but rather counts it as if it only existed on one page, then we already know the answer to that person’s question, which is that no, it’s not going to be a big deal because it’s a navigational sitewide link, which is not meaningful other than to tell Google that it’s an important page for the site.

A sitewide navigational link is important but it’s not the same as a contextual link from within content. A contextual link has meaning, it’s meaningful, because it says something about the page being linked to. One is not better than the other, they’re just different kinds of links.

This is the question that the SEO asked:

Hey
@johnmu.com
a client has 4 navs. A Main Menu, Footer Links, Sidebar Quicklinks & a Related Pages Mini-Nav in posts. Not for SEO but they have quadrupled the internal link profile to a key page on a single anchor.

Any risk that we’re diluting the ability to rank that keyword with “overuse”?

Someone else answered the question with a link to a Search Engine Journal article that was about a site that contains links to every page of the entire site, which is a different situation entirely. That’s a type of site architecture from the old days called a flat site architecture. It was created by SEOs for the purpose of spreading PageRank across to all pages of the site to help all them rank.

Google’s John Mueller responded with a comment about that flat site structure and an answer to the query posed by the SEO:

“I think (it’s been years) that was more about a site that links from all pages to all pages, where you lose context of how pages sit within the site. I don’t think that’s your situation. Having 4 identical links on a page to another page seems fine & common to me, I wouldn’t worry about that.”

Lots Of Duplication

The SEO responded that the duplicated content along the sidebars were HTML and not “navigation” and that they were concerned that this introduced a lot of duplication.

He wrote:

“Its 4 duplicated navs on every page of the site, semantically the side bar and related pages are not navs, they’re html, list structured links so lots of duplication IMO”

I think that Mueller’s answer still applies. It doesn’t matter if they are semantically side bars and related pages. What’s important is that they are not the main content, which is what Google is focused on.

Google’s Martin Splitt went into detail about this four years ago where he talked about the Centerpiece Annotation.

Martin talks about how they identify related content links and other stuff that’s not the main content:

“And then there’s this other thing here, which seems to be like links to related products but it’s not really part of the centerpiece. It’s not really main content here. This seems to be additional stuff.

And then there’s like a bunch of boilerplate or, “Hey, we figured out that the menu looks pretty much the same on all these pages and lists.”

So the answer for the SEO is that it doesn’t matter if those links are in a sidebar or menu navigation or related links. Google identifies it as not the main content and for the purposes of analyzing the web page, sets that aside. Google doesn’t care if stuff is popping up all over the site, it’s not main content.

Read the original discussion on Bluesky.

Featured Image by Shutterstock/Photobank.kiev.ua

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Apr 29 2025

We Figured Out How AI Overviews Work [& Built A Tool To Prove It] via @sejournal, @mktbrew

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

Wondering how to realign your SEO strategy for maximum SERP visibility in AI Overviews (AIO)?

Do you wish you had techniques that mirror how AI understands relevance?

Imagine if Google handed you the blueprint for AI Overviews:

Every signal.
Every scoring mechanism.
Every semantic pattern it uses to decide what content makes the cut.

That’s what our search engineers did.

They reverse-engineered how Google’s AI Overviews work and built a model that shows you exactly what to fix.

It’s no longer about superficial tweaks; it’s about aligning with how AI truly evaluates meaning and relevance.

In this article, we’ll show you how to rank in AIO SERPs by creating embeddings for your content and how to realign your content for maximum visibility by using AIO tools built by search engineers.

The 3 Key Features Of AI Overviews That Can Make Or Break Your Rankings

Let’s start with the basic building blocks of a Google AI Overviews (AIO) response:

What Are Embeddings?

Embeddings are high-dimensional numerical representations of text. They allow AI systems to understand the meaning of words, phrases, or even entire pages, beyond just the words themselves.

Rather than matching exact terms, embeddings turn language into vectors, or arrays of numbers, that capture the semantic relationships between concepts.

For example, “car,” “vehicle,” and “automobile” are different words, but their embeddings will be close in vector space because they mean similar things.

Large language models (LLMs) like ChatGPT or Google Gemini use embeddings to “understand” language; they don’t just see words, they see patterns of meaning.

Image created by MarketBrew.ai, April 2025

Why Do Embeddings Matter For SEO?

Understanding how Large Language Models (LLMs) interpret content is key to winning in AI-driven search results, especially with Google’s AI Overviews.

Search engines have shifted from simple keyword matching to deeper semantic understanding. Now, they rank content based on contextual relevance, topic clusters, and semantic similarity to user intent, not just isolated words.

Image created by MarketBrew.ai, April 2025

Embeddings power this evolution.

They enable search engines to group, compare, and rank content with a level of precision that traditional methods (like TF-IDF, keyword density, or Entity SEO) can’t match.

By learning how embeddings work, SEOs gain tools to align their content with how search engines actually think, opening the door to better rankings in semantic search.

Image created by MarketBrew.ai, April 2025

How To Rank In AIO SERPs By Creating Embeddings

Step 1: Set Up Your OpenAI Account

Sign Up or Log In: If you haven’t already, sign up for an account on OpenAI’s platform at https://platform.openai.com/signup.
API Key: Once logged in, you’ll need to generate an API key to access OpenAI’s services. You can find this in your account settings under the API section.

Step 2: Install The OpenAI Python Client To Simplify This Step For SEO Pros

OpenAI provides a Python client that simplifies the process of interacting with their API. To install it, run the following command in your terminal or command prompt:

pip install openai

Step 3: Authenticate With Your API Key

Before making requests, you need to authenticate using your API key. Here’s how you can set it up in your Python script:

import openai

openai.api_key = 'your-api-key-here'

Step 4: Choose Your Embedding Model

At the time of this article’s creation, OpenAI’s text-embedding-3-small is considered one of the most advanced embedding models. It is highly efficient for a wide range of text processing tasks.

Step 5: Create Embeddings For Your Content

To generate embeddings for text:

response = openai.Embedding.create(

model="text-embedding-3-small",

input="This is an example sentence."

)

embeddings = response['data'][0]['embedding']

print(embeddings)

The result is a list of numbers representing the semantic meaning of your input in high-dimensional space.

Step 6: Storing Embeddings

Store embeddings in a database for future use; tools like Pinecone or PostgreSQL with pgvector are great options.

Step 7: Handling Large Text Inputs

For large content, break it down into paragraphs or sections and generate embeddings for each chunk.

Use similarly sized chunks for better cosine similarity calculations. To represent an entire document, you can average the embeddings for each chunk.

💡Pro Tip: Use Market Brew’s free AI Overviews Visualizer. The search engineer team at Market Brew has created this visualizer to help you understand exactly how embeddings, the fourth generation of text classifiers, are used by search engines.

Semantics: Comparing Embeddings With Cosine Similarity

Cosine similarity measures the similarity between two vectors (embeddings), regardless of their magnitude.

This is essential for comparing the semantic similarity between two pieces of text.

Image created by MarketBrew.ai, April 2025

Typical search engine comparisons include:

Keywords with paragraphs,
Groups of paragraphs with other paragraphs, and
Groups of keywords with groups of paragraphs.

Next, search engines cluster these embeddings.

How Search Engines Cluster Embeddings

Search engines can organize content based on clusters of embeddings.

In the video below, we are going to illustrate why and how you can use embedding clusters, using Market Brew’s free AI Overviews Visualizer, to fix content alignment issues that may be preventing you from appearing in Google’s AI Overviews or even their regular search results!

Embedding clusters, or “semantic clouds”, form one of the most powerful ranking tools for search engineers today.

Semantic clouds are topic clusters in thousands of dimensions. The illustration above shows a 3D representation to simplify understanding.

Topic clusters are to entities as semantic clouds are to embeddings. Think of a semantic cloud as a topic cluster on steroids.

Search engineers use this like they do topic clusters.

When your content falls outside the top semantic cloud – what the AI deems most relevant – it is ignored, demoted, or excluded from AI Overviews (and even regular search results) entirely.

No matter how well-written or optimized your page might be in the traditional sense, it won’t surface if it doesn’t align with the right semantic cluster that the finely tuned AI system is seeking.

By using the AI Overviews Visualizer, you can finally see whether your content aligns with the dominant semantic cloud for a given query. If it doesn’t, the tool provides a realignment strategy to help you bridge that gap.

In a world where AI decides what gets shown, this level of visibility isn’t just helpful. It’s essential.

Free AI Overviews Visualizer: How To Fix Content Alignment

Step 1: Use The Visualizer

Input your URL into this AI Overviews Visualizer tool to see how search engines view your content using embeddings. The Cluster Analysis tab will display embedding clusters for your page and indicate whether your content aligns with the correct cluster.

Screenshot from MarketBrew.ai, April 2025

Step 2: Read The Realignment Strategy

The tool provides a realignment strategy if needed. This provides a clear roadmap for adjusting your content to better align with the AI’s interpretation of relevance.

Example: If your page is semantically distant from the top embedding cluster, the realignment strategy will suggest changes, such as reworking your content or shifting focus.

Screenshot from MarketBrew.ai, April 2025

Example of New Page Content Aligned with Target Embedding

Screenshot from MarketBrew.ai, April 2025

Step 3: Test New Changes

Use the “Test New Content” feature to check how well your content now fits the AIO’s top embedding cluster. Iterative testing and refinement are recommended as AI Overviews evolve.

Screenshot by MarketBrew.ai, April 2025

See Your Content Like A Search Engine & Tune It Like A Pro

You’ve just seen under the hood of modern SEO – embeddings, clusters, and AI Overviews. These aren’t abstract theories. They’re the same core systems that Google uses to determine what ranks.

Think of it like getting access to the Porsche service manual, not just the owner’s guide. Suddenly, you can stop guessing which tweaks matter and start making adjustments that actually move the needle.

At Market Brew, we’ve spent over two decades modeling these algorithms. Tools like the free AI Overviews Visualizer give you that mechanic’s-eye view of how search engines interpret your content.

And for teams that want to go further, a paid license unlocks Ranking Blueprints to help track and prioritize which AIO-based metrics most affect your rankings – like cosine similarity and top embedding clusters.

You have the manual now. The next move is yours.

Reveal What Google’s AI Really Sees

Image Credits

Featured Image: Image by Market Brew. Used with permission.

In-Post Image: Images by Market Brew. Used with permission.

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Apr 29 2025

Google Discover Desktop Data Already Trackable In Search Console via @sejournal, @MattGSouthern

Google Discover desktop data is already trackable in Search Console. Here’s how to prepare ahead of the full rollout.

Data suggests Google Discover has been in testing on desktop for over 16 months.
Desktop Discover data reveals lower traffic than mobile (only 4% in the US).
Publishers can access their desktop Discover performance now in Search Console.

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Apr 29 2025

Google Clarifies Googlebot-News Crawler Documentation via @sejournal, @martinibuster

Google updated their Google News crawler documentation to correct an error that implied that publisher crawler preferences addressed to Googlebot-News influenced the News tab in Google search.

Google News Tab

The Google News tab is a category of search that is displayed near the top of the search results pages (SERPs). The news tab filter displays search results from news publishers. Content that’s shown in the news tab generally comes from sites that eligible to be displayed in Google News and must meet Google’s news content policies.

What Changed:

Google’s changelog noted that the user agent description was in error to say that publisher preferences influenced what’s shown in the Google News tab.

They explained:

“The description for how crawling preferences addressed to Googlebot-News mistakenly stated that they’d affect the News tab on Google, which is not the case.”

The entire section mentioning the News tab in Google Search was removed from this sentence:

“Crawling preferences addressed to the Googlebot-News user agent affect all surfaces of Google News (for example, the News tab in Google Search and the Google News app).”

The corrected version now reads:

“Crawling preferences addressed to the Googlebot-News user agent affect the Google News product, including news.google.com and the Google News app.”

Read the updated Googlebot-News user agent documentation here:

Googlebot News

Featured Image by Shutterstock/Asier Romero

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Apr 28 2025

Google Updates Gemini/Vertex AI User Agent Documentation via @sejournal, @martinibuster

Google updated the documentation for the Google-Extended user agent, which publishers can use to control whether Google Gemini and Vertex use their data for training purposes or for grounding AI answers.

Updated Guidance

Google updated their guidance on Google-Extended based on publisher feedback for the purpose of improving clarity and adding more specific details.

Previous Documentation:

“Google-Extended is a standalone product token that web publishers can use to manage whether their sites help improve Gemini Apps and Vertex AI generative APIs, including future generations of models that power those products. Grounding with Google Search on Vertex AI does not use web pages for grounding that have disallowed Google-Extended.”

Updated Version

The updated documentation provides more detail and is easier to understand explanation of what the user agent is for and what blocking it accomplishes.

“Google-Extended is a standalone product token that web publishers can use to manage whether content Google crawls from their sites may be used for training future generations of Gemini models that power Gemini Apps and Vertex AI API for Gemini and for grounding (providing content from the Google Search index to the model at prompt time to improve factuality and relevancy) in Gemini Apps and Grounding with Google Search on Vertex AI.”

Google-Extended Is Not A Ranking Signal

Google also updated one sentence to make it clear that Google-Extended isn’t used as a ranking signal for Google Search. That means that allowing Google-Extended to use the data for grounding Gemini AI answers won’t be counted as a ranking signal.

Grounding is a reference to using web data (and knowledge base data) to improve answers provided by a large language model with up to date and factual information, helping to avoid fabrications (also known as hallucinations).

The previous version omitted mention of ranking signals:

“Google-Extended does not impact a site’s inclusion or ranking in Google Search.”

The newly updated version specifically mentions Google-Extended in the context of a ranking signas:

“Google-Extended does not impact a site’s inclusion in Google Search nor is it used as a ranking signal in Google Search.”

Documentation Matches Other Guidance

The updated documentation matches a short passage about Google-Extended that’s elsewhere in Google Search Central. The other longstanding guidance explains that Google-Extended is not a way to control how website information is shown in Google Search, demonstrating how Google-Extended is separated from Google Search.

Here’s the other guidance that’s found on a page about preventing content from appearing in Google AI Overviews:

“Google-Extended is not a method for managing how your content appears in Google Search. Instead, use other methods to manage your content in Search, such as robots.txt or other robot controls.”

Takeaways

Google-Extended Documentation Update:
The Google-Extended documentation was clarified and expanded to make its purpose and effects easier to understand.
Separation From Ranking Signals:
The updated guidance explicitly states that Google-Extended does not affect Google Search inclusion nor is it a ranking signal.
Internal Use By AI Models:
Clarified that Google-Extended controls whether site content is used for training and grounding Gemini models.
Consistency Across Documentation:
The updated language now matches longstanding guidance elsewhere in Google’s documentation, reinforcing its separation from search visibility controls.

Google updated its Google-Extended documentation to explain that publishers can block their content from being used for AI training and grounding without affecting Google Search rankings. The update also matches longstanding guidance that explains Google-Extended has no effect on how sites are indexed or ranked in Search.

Featured Image by Shutterstock/JHVEPhoto

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Apr 24 2025

Google’s AI Overviews Reach 1.5 Billion Monthly Users via @sejournal, @MattGSouthern

Google’s AI search features have reached widespread adoption. The company announced that AI Overviews in Search now reach 1.5 billion users per month.

This information was revealed during Alphabet’s Q1 earnings call.

Alphabet Earnings Show Growth Across Core Products

Alphabet announced strong financial results for Q1, highlighting the adoption of AI across its products. The company reported total revenue of $90.2 billion, representing a 12% year-over-year increase.

Despite industry concerns that AI will disrupt traditional search models, Google reported that Search revenue grew 10% year-over-year to $50.7 billion.

Pichai said in the earnings report:

“We’re pleased with our strong Q1 results, which reflect healthy growth and momentum across the business. Underpinning this growth is our unique full stack approach to AI. This quarter was super exciting as we rolled out Gemini 2.5, our most intelligent AI model, which is achieving breakthroughs in performance and is an extraordinary foundation for our future innovation.

Search saw continued strong growth, boosted by the engagement we’re seeing with features like AI Overviews, which now has 1.5 billion users per month. Driven by YouTube and Google One, we surpassed 270 million paid subscriptions. And Cloud grew rapidly with significant demand for our solutions.”

Earnings Highlights

Alphabet’s Q1 earnings report showed healthy performance across most business segments:

Total revenue: $90.2 billion, up 12% year-over-year
Operating income: $30.6 billion, up 20% year-over-year
Operating margin: Expanded by two percentage points to 34%
Google Search revenue: $50.7 billion, up 10% year-over-year
YouTube ad revenue: $8.9 billion, up 10% year-over-year
Google Cloud revenue: $12.3 billion, up 28% year-over-year
Cloud operating margin: Improved to 17.8% from 9.4% last year
Capital expenditures: $17.2 billion, up 43% year-over-year

One notable underperformer was Google Network revenue, which declined 2% year-over-year to $7.3 billion, suggesting potential challenges in display advertising.

Google Cloud: A Standout

Google Cloud emerged as a standout performer, with revenue growing 28% to $12.3 billion.

The jump in profitability was more impressive, with operating income rising to $2.2 billion (a 17.8% margin) compared to $900 million (a 9.4% margin) in the same quarter of the previous year.

“Cloud grew rapidly with significant demand for our solutions,” noted Pichai, with the earnings report highlighting strong performance across core GCP products, AI Infrastructure, and Generative AI Solutions.

Implications for Search Marketers

For SEO professionals, the earnings data points to several key considerations:

Google’s successful integration of AI, while maintaining Search revenue growth, indicates that AI Overviews will likely expand further.
The combined 1.5 billion monthly AI Overviews impressions, along with continued investment, suggest that this shift in search presentation is likely to be permanent.
Google’s operating margin has improved despite significant investments in AI, providing the company with a financial incentive to continue this strategy.

Looking Forward

Alphabet’s Q1 results demonstrate that the company is successfully navigating the transition to AI-enhanced products while maintaining revenue growth.

For search marketers, the financial strength behind Google’s AI initiatives suggests these changes to search will accelerate rather than slow down.

With 1.5 billion users already experiencing AI Overviews monthly and Google’s continued heavy investment in AI infrastructure, the search landscape is undergoing profound changes, which are now reflected in the company’s financial performance.

Featured Image: Ifan Apriyana/Shutterstock

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Apr 24 2025

Winning The Link Game: How To Create & Pitch Content That Attracts Incredible Links [Webinar] via @sejournal, @hethr_campbell

Think link building is dead? Think again.

In 2025, the backlink game has changed. If your strategy hasn’t, you might be stuck chasing low-impact backlinks that barely move the needle.

Top brands are winning big by creating linkable assets that earn high-quality links and boost search rankings. Want in?

Join us for our next webinar: “Winning The Link Game: How To Create & Pitch Content That Attracts Incredible Links” with Michael Johnson of Resolve.

Why This Webinar Is A Must-Attend Event:

We’ll break down the exact strategies used by leading brands to turn content into backlinks from authoritative sites.

What You’ll Learn:

Link Diversity & Relevance: Why a strategic mix of backlinks is more important than ever.
Digital PR That Works: How to pitch like a pro and secure links that matter.
Campaign Frameworks You Can Use: Step-by-step guidance to build your own winning digital PR strategy.

Why You Shouldn’t Miss This:

Get an exclusive demo of a ChatGPT prompt that generates custom digital PR concepts from just a URL.

Live with Michael Johnson, Sr. Strategist at Resolve, as he walks you through actionable techniques that deliver real results.

Can’t attend live? No problem! Sign up anyway, and we’ll send you the recording.

Let’s upgrade your link strategy. See you there!

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