Nov 6 2025
OpenAI’s Sam Altman Raises Possibility Of Ads On ChatGPT via @sejournal, @martinibuster

OpenAI’s CEO Sam Altman sat for an interview where he explained that his vision for the future of ChatGPT is as a trusted assistant that’s user-aligned, saying that booking hotels is not going to be the way to monetize “the world’s smartest model.” He pointed to Google as an example of what he doesn’t want ChatGPT to become: a service that accepts advertising dollars to place the worst choice above the best choice. He then followed up to express openness to advertising.

User-Aligned Monetization Model

Altman contrasted OpenAI’s revenue approach with the ad-driven incentives of Google. He explained that Google’s Search and advertising ecosystem depends on Google’s search results “doing badly for the user,” because ranking decisions are partly tied to maximizing advertising income.

The interviewer related that he and his wife took a trip to Europe and booked multiple hotels with help from ChatGPT and ate at restaurants that ChatGPT helped him find and at no point did any kind of kickback or advertising fee go back to OpenAI, leading him to tell his wife that ChatGPT “didn’t get a dime from this… this just seems wrong….” because he was getting so much value from ChatGPT and ChatGPT wasn’t getting anything back.

Altman answered that users trust ChatGPT and that’s why so many people pay for it.

He explained:

“I think if ChatGPT finds you the… To zoom out even before the answer, one of the unusual things we noticed a while ago, and this was when it was a worst problem, ChatGPT would consistently be reported as a user’s most trusted technology product from a big tech company. We don’t really think of ourselves as a big tech company, but I guess we are now. That’s very odd on the surface, because AI is the thing that hallucinates, AI is the thing with all the errors, and that was much more of a problem. And there’s a question of why.

Ads on a Google search are dependent on Google doing badly. If it was giving you the best answer, there’d be no reason ever to buy an ad above it. So you’re like, that thing’s not quite aligned with me.

ChatGPT, maybe it gives you the best answer, maybe it doesn’t, but you’re paying it, or hopefully are paying it, and it’s at least trying to give you the best answer. And that has led to people having a deep and pretty trusting relationship with ChatGPT. You ask ChatGPT for the best hotel, not Google or something else.”

Altman’s response used the interviewer’s experience as an example of a paradigm change in user trust in technology. He contrasted ChatGPT’s model, where users directly pay for answers, with Google’s ad-based model that profits from imperfect results. His point is that ChatGPT’s business model aligns more closely with users’ interests, earning a sense of trust and reliability rather than making their users feel exploited by an advertising system. This is why users perceive ChatGPT as more trustworthy, even though ChatGPT is known to hallucinate.

Altman Is Open To Transaction Fees

Altman was strongly against accepting advertising money in exchange for showing a hotel above what ChatGPT would naturally show. He said that he would be open to accepting a transaction fee should a user book that hotel through ChatGPT because that has no influence on what ChatGPT recommends, thus preserving a user’s trust.

He shared how this would work:

“If ChatGPT were accepting payment to put a worse hotel above a better hotel, that’s probably catastrophic for your relationship with ChatGPT. On the other hand, if ChatGPT shows you it’s best hotel, whatever that is, and then if you book it with one click, takes the same cut that it would take from any other hotel, and there’s nothing that influenced it, but there’s some sort of transaction fee, I think that’s probably okay. And with our recent commerce thing, that’s the spirit of what we’re trying to do. We’ll do that for travel at some point.”

I think a takeaway here is that Altman believes the advertising model that the Internet has been built on over the past thirty-plus years can subvert user trust and lead to a poor user experience. He feels that a transaction fee model is less likely to impact the quality of the service that users are paying for and that it will maintain the feeling of trust that people have in ChatGPT.

But later on in the interview, as you’ll see, Altman surprises the interviewer with his comment about the possibility of advertisements on ChatGPT.

How OpenAI Will Monetize Itself

When pressed about how OpenAI will monetize itself, Altman responded that he expects the future of commerce will have lower margins and that he doesn’t expect to fully fund OpenAI by booking hotels but by doing exceptional things like curing diseases.

Altman explained his vision:

“So one thing I believe in general related to this is that margins are going to go dramatically down on most goods and services, including things like hotel bookings. I’m happy about that. I think there’s like a lot of taxes that just suck for the economy and getting those down should be great all around. But I think that most companies like OpenAI will make more money at a lower margin.

…I think the way to monetize the world’s smartest model is certainly not hotel booking.  …I want to discover new science and figure out a way to monetize that. You can only do with the smartest model.

There is a question of, should, many people have asked, should OpenAI do ChatGPT at all? Why don’t you just go build AGI? Why don’t you go discover a cure for every disease, nuclear fusion, cheap rockets, the whole thing, and just license that technology? And it is not an unfair question because I believe that is the stuff that we will do that will be most important and make the most money eventually.

…Maybe some people will only ever book hotels and not do anything else, but a lot of people will figure out they can do more and more stuff and create new companies and ideas and art and whatever.

So maybe ChatGPT and hotel booking and whatever else is not the best way we can make money. In fact, I’m certain it’s not. I do think it’s a very important thing to do for the world, and I’m happy for OpenAI to do some things that are not the economic maxing thing.”

Advertisements May Be Coming To ChatGPT

At around the 18 minute mark the interviewer asked Altman about advertising on OpenAI and Altman acknowledged that there may be a form of advertising but was vague about what that would look like.

He explained:

“Again, there’s a kind of ad that I think would be really bad, like the one we talked about.

There are kinds of ads that I think would be very good or pretty good to do. I expect it’s something we’ll try at some point. I do not think it is our biggest revenue opportunity.”

The interviewer asked:

“What will the ad look like on the page?”

Altman responded:

“I have no idea. You asked like a question about productivity earlier. I’m really good about not doing the things I don’t want to do.”

Takeaway

Sam Altman suggests an interesting way forward on how to monetize Internet users. His way is based on trust and finding a way to monetize that doesn’t betray that trust.

Watch the interview starting at about the 16 minute mark:

Featured image/Screenshot from interview

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Nov 5 2025
Perplexity Bets $400M On Snapchat To Scale AI Search Adoption via @sejournal, @MattGSouthern

Perplexity will pay Snap $400 million to integrate its AI answer engine into Snapchat’s chat interface, with rollout starting next year.

  • Perplexity will pay Snap $400 million over one year to integrate its AI answer engine into Snapchat.
  • Snap calls this its first large-scale integration of an external AI partner directly in the app.
  • Perplexity handles 150+ million questions weekly, so the integration meaningfully expands distribution.
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Nov 5 2025
Google Deprecates Practice Problem Structured Data In Search via @sejournal, @MattGSouthern

Google will deprecate practice problem structured data in January and clarifies Dataset markup is only for Dataset Search. Book actions remain supported.

  • Practice problem markup is being deprecated from Google Search in January.
  • Dataset structured data is for Dataset Search only; it isn’t used in Google Search.
  • Book actions continue to work in Google Search.
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Nov 5 2025
YouTube Separates Organic & Paid Metrics In Channel Analytics via @sejournal, @MattGSouthern

YouTube introduced separate filtering for organic and paid traffic metrics in YouTube Analytics, allowing you to distinguish between unpaid and promoted content performance.

Channels can now filter views, engaged views, likes, comments, shares, and watchtime by traffic source. The update addresses longstanding questions about how paid advertising affects organic channel growth.

YouTube’s announcement included clarification that advertising doesn’t negatively impact organic performance, stating the two systems operate independently.

What’s New

The Analytics update adds traffic source filtering across core engagement metrics.

You can view performance data split between organic sources and paid advertisements, including YouTube Promote campaigns and brand-sponsored content.

YouTube’s announcement stated:

“Organic performance is determined by how the platform’s algorithm recommends your video to viewers based on factors like watch time, engagement, and audience retention. This is your video’s word of mouth reach, determined by the quality of the content itself. Whether or not it also runs as an ad has no impact.”

The platform distinguishes paid ad performance as determined by budget and targeting settings rather than algorithmic recommendations.

This is explained in more detail in the video below:

Addressing Performance Questions

YouTube addressed creator concerns about lower aggregate metrics when combining organic and paid performance.

The announcement noted that advertising often targets new audiences who may engage at lower rates than existing subscribers, which can reduce overall retention and click-through metrics when viewed in aggregate.

The new filtering allows creators to analyze each traffic source separately rather than viewing combined data.

Why This Matters

You can now measure organic content performance without paid promotion data affecting your metrics.

This separation helps identify which growth strategies work independently rather than attributing paid gains to organic strategy or vice versa.

The filtering clarifies whether audience retention issues stem from content quality or new audience targeting in ad campaigns.

Looking Ahead

The traffic filtering feature is available now in YouTube Analytics. YouTube didn’t specify whether additional metrics or filtering options will be added to the organic versus paid breakdown.

The update coincides with YouTube’s October 2025 terminology change renaming the “Views” metric to “TrueView views” in Google Ads reporting, though this naming change doesn’t affect how views are counted or billed.

Featured Image: T. Schneider/Shutterstock

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Nov 5 2025
AI SEO: How To Understand AI Mode Rankings via @sejournal, @martinibuster

A simplified explanation of how Google ranks content is that it is based on understanding search queries and web pages, plus a number of external ranking signals. With AI Mode, that’s just the starting point for ranking websites. Even keywords are starting to go away, replaced by increasingly complex queries and even images. How do you optimize for that? The following are steps that can be taken to help answer that question.

Latent Questions Are A Profound Change To SEO

The word “latent” means something that exists but cannot be seen.  When a user issues a complex query the LLM must not only understand the query but also map out follow-up questions that a user might ask as part of an information journey about the topic. Those questions that comprise the follow-up questions are latent questions. Virtually every query contains latent questions.

Google’s Information Gain Patent

The issue of latent queries poses a new problem for SEO: How do you optimize for questions that are unknown? Optimizing for AI search means optimizing for the entire range of questions that are related to the initial or head query.

But even the concept of a head query is going away because users are now asking complex queries which demand complex answers. This is precisely why it may be useful for AI SEO purposes to optimize not just for one query but for the immediate information needs of the user.

How does Google understand the information need that’s hidden within a user’s query? The answer is found in Google’s Information Gain Patent. That patent is about ranking a web page that is relevant for a query then afterward ranking other web pages that have different but related content.

Identify The Latent (Hidden) Questions

One way to look at AI search results is to break them down into the questions that the AI answers are satisfying, to identify the hidden query fan-out questions.

For example, if you ask Google’s AI Mode how to make pizza dough the AI Mode will generate a pizza dough recipe. The answer is synthesized from multiple queries.

There is a way to extract the hidden (latent) questions by using a Reverse Question Answering Prompt, which I’ll show below.

Here is an example of an initial query and the additional query fan-out questions that are involved:

Initial Query: How to make pizza dough?

Query Fan-Out Questions:

  • What ingredients are needed to make pizza dough?
  • How much pizza dough does this recipe make?
  • What indicates that the yeast is active?
  • How long should the dough be kneaded by hand?
  • How long should the dough be kneaded with a mixer?
  • How long should the dough rise during the first proofing?
  • How can you develop more complex flavor in the dough?
  • What should the oven temperature be for baking the pizza?
  • How long should the pizza bake?

Actual AI Mode Question And Answers

Here are some of the hidden query fan-out questions accompanied by the answers that Google’s AI Mode generated.

Question: What ingredients are needed to make pizza dough?
Answer: “1 and 1/3 cups warm water (around 105-110°F or 40-43°C); 1 tablespoon granulated sugar (or honey); 1 packet active dry or instant yeast (2 1/4 teaspoons); 3 1/2 to 4 cups all-purpose or bread flour, plus more for dusting; 2 tablespoons extra-virgin olive oil, plus more for greasing; 1 teaspoon salt.”

Question: How much pizza dough does this recipe make?
Answer: “This recipe makes enough dough for two 10-12 inch pizzas.”

Question: What indicates that the yeast is active?
Answer: “Cover and let it sit for about 5-10 minutes, until the mixture is foamy and bubbly. This indicates the yeast is active.”

Screenshot Of The Actual AI Mode Answer

Screenshot of AI Mode answer for query

Reverse Question Answering Prompt

You can use the Reverse Question Answering Prompt to identify the underlying questions in any AI Mode answer. You can even use a similar but more precise prompt to analyze your own content to identify what questions the document answers. It’s a good way to check if your content does or does not answer the questions you want it to answer.

Prompt To Extract Questions From AI Mode

Here is the prompt to use to extract the hidden questions within an AI Mode answer:

Analyze the document and extract a list of questions that are directly and completely answered by full sentences in the text. Only include questions if the document contains a full sentence or sentences that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference.

For each question, ensure that it is a clear and concise restatement of the exact information present. This is a reverse question generation task: only use the content already present in the document.

For each question, also include the exact sentences from the document that answer it. Only generate questions that have a complete, direct answer in the form of a full sentence or sentences in the document.

Reverse Question Answering Analysis For Web Content

The previously described prompt can be used to extract the questions that are answered by your own or a competitor’s content. But it will not differentiate between the core search queries the document is relevant for and other questions that are ancillary to the main topic.

To do a Reverse Question Answering analysis with your own content, try this more precise variant of the prompt:

Analyze the document and extract a list of questions that are core to the document’s central topic and are directly and completely answered by full sentences in the text.

Only include questions if the document contains a full sentence or contiguous sentences that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference. Crucially, exclude any questions about supporting anecdotes, personal asides, or general background information that is not the main subject of the document.

For each question, ensure that it is a clear and concise restatement of the exact information present. This is a reverse question generation task: only use the content already present in the document.

For each question, also include the exact sentences from the document that answer it. Only generate questions that have a complete, direct answer in the form of a full sentence or sentences in the document.

The above prompt is meant to emulate how an LLM or information retrieval system might extract the core questions that a web document answers, while ignoring the parts of the document that aren’t central to its informational purpose, such as tangential commentary that do not directly contribute to the document’s main topic or purpose.

Cultivate Being Mentioned On Other Sites

Something that is becoming increasingly apparent is that AI search tends to rank companies whose websites are recommended by other sites. Research by Ahrefs found a strong correlation between sites that appear in AI Overviews and branded mentions.

According to Ahrefs:

“So we looked at these factors that correlate with the amount of times a brand appears in AI overviews, tested tons of different things, and by far the strongest correlation, very, very strong correlation, almost 0.67, was branded web mentions.

So if your brand is mentioned in a ton of different places on the web, that correlates very highly with your brand being mentioned in lots of AI conversations as well.”

Read: Data Shows Brand Mentions Boost AI Search Rankings

This finding strongly suggests that visibility in AI search may depend less on backlinks and more on how often a brand is discussed across the web. AI models seem to learn which brands are recommended by how often those sites are mentioned across other sites, including sites like Reddit.

Post-Keyword Ranking Era

We are in a post-keyword ranking era. Google’s organic search was already using AI and a core topicality system to better understand queries and the topic that web pages were about. The big difference now is that Google’s AI Mode has enabled users to search with long and complex conversational queries that aren’t necessarily answered by web pages that are focused on being relevant to keywords instead of to what people are actually looking for.

Write About Topics

Writing about topics seems like a straightforward approach but what it means depends on the context of the topic.

What “topic writing” proposes is that instead of writing about the keyword Blue Widget, the writer must write about the topic of Blue Widget.

The old way of SEO was to think about Blue Widget and all the associated Blue Widget keyword phrases:

Associated keyword phrases

  • How to make blue widgets
  • Cheap blue widgets
  • Best blue widgets

Images And Videos

The up to date way to write is to think in terms of answers and helpfulness. For example, do the images on a travel site communicate what a destination is about? Will a reader linger on the photo? On a product site, do the images communicate useful information that will help a consumer determine if something will fit and what it might look like on them?

Images and videos, if they’re helpful and answer questions, could become increasingly important as users begin to search with images and increasingly expect to see more videos in the search results, both short and longform videos.

Read:

Featured Image by Shutterstock/Nithid

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Nov 4 2025
Google AI Mode Starts Rolling Out Agentic Booking In Labs via @sejournal, @MattGSouthern

Google is starting to roll out agentic capabilities in AI Mode as a Search Labs experiment.

This update enables AI Mode to find and book restaurant reservations, event tickets, and wellness appointments across multiple websites.

Availability is limited, and Google notes this experiment may not be available to everyone yet.

Robby Stein, VP of Product at Google Search, announced the rollout on X.

What’s New

AI Mode now performs multi-site searches for three booking categories and returns real-time options with a curated list of time slots or ticket prices.

Here’s what U.S. users see when visiting the landing page in Search Labs:

Screenshot from: labs.google.com/search/experiment/43, November 2025.
Screenshot from: labs.google.com/search/experiment/43, November 2025.

Restaurant Reservations

You can ask for party size, time, neighborhood, or cuisine.

Google’s example:

“find me a dinner reservation for 3 people this Friday after 6pm around Logan Square. craving ramen or bibimbap.”

Results include available times with links to book.

Event Tickets

Google AI Pro and Ultra subscribers can search for concert and event tickets with price and seating preferences, for example:

“find me 2 cheap tickets for the Shaboozey concert coming up. prefer standing floor tickets.”

Wellness Appointments

Also for Pro and Ultra subscribers, AI Mode can surface real-time availability from local service booking platforms and link you to complete the appointment.

How It Works

AI Mode searches across multiple websites to surface real-time availability, then presents a curated list. It links you directly to the provider’s booking page to finalize the reservation or purchase.

Requirements

Full functionality requires:

  • A personal Google Account you manage yourself
  • Web & App Activity turned on
  • Search Labs access
  • U.S. location and English language
  • Age 18 or older

These conditions are listed on the experiment page.

Why This Matters

If you handle bookings, AI Mode can reduce the steps it takes for people to compare times and prices across sites.

You still complete the transaction on the provider’s site, but discovery and comparison move into a single query.

Looking Ahead

Google calls this an early experiment that may make mistakes and invites feedback to improve quality.

Rollout is staged, so availability will expand over the coming days.

Featured Image: Koshiro K/Shutterstock

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Nov 4 2025
Ahrefs Data Shows Brand Mentions Boost AI Search Rankings via @sejournal, @martinibuster

The latest Ahrefs podcast shares data showing that brand mentions on third-party websites help improve visibility across AI search surfaces. What they found is that brand mentions correlate strongly with ranking better in AI search, indicating that we are firmly in a new era of off-page SEO.

Training Data Gets Cited

Tim Soulo, CMO of Ahrefs, said that off-page activity that increases being mentioned on other sites improves visibility in AI search results, both those based on training data and those drawing from live search results. The benefits of conducting off-page SEO apply to both. The only difference is that training data doesn’t get into LLMs right away.

Tim recommends identifying where your industry gets mentioned:

“You just need to see like where your competitors are mentioned, where you are mentioned, where your industry is mentioned.

And you have to get mentions there because then if the AI chatbot would do a search and find those pages and create their answer based on what they see on those pages, this is one thing.

But if some of the AI providers will decide to retrain their entire model on a more recent snapshot of the web, they will use essentially the same pages.”

Tim cautioned that AI companies don’t ingest new web data for training and that there’s a lag in months between how often large language models receive fresh training data from the web.

Appear On Authoritative Websites

Although Tim did not mention specific tactics for obtaining brand mentions, in my opinion, off-page link-building strategies don’t have to change much to build brand mentions.

Tim underlined the importance of appearing on authoritative websites:

“So yeah, …essentially it’s not that you have to use different tactics for those things. You do the same thing, you appear like on credible websites, but yeah, let’s continue.”

The only thing that I would add is that authoritativeness in this situation is if a site gets mentioned by AI search. But the other thing to think about is if a site is simply the go-to for a particular kind of information, relevance

Topicality Of Brand Mentions

The other thing that was discussed is the topicality of the brand mentions, meaning the context in which the brand is discussed. Ryan Law, Ahrefs’ Director of Content Marketing, said that the context of the brand mention is important, and I agree. You can’t always control the narrative, but that’s where old-fashioned PR outreach comes in, where you can include quotes and so on to build the right context.

Law explained:

“Well, that segues very nicely to what I think is probably the most useful discrete tactic you can do, and that is building off-site mentions.

A big part of how LLMs understand what your brand is about and when it should recommend it and the context it should talk about you is based on where you appear in its training data and where you appear on the web.

  • What topics are you commonly mentioned alongside?
  • What other brands are you mentioned alongside?

I think Patrick Stox has been referring to this as the era of off-page SEO. In some ways, the content on your own site is not as valuable as the content about you on other pages on the web.”

Law mentioned that these off-page mentions don’t have to be in the form of links in order to be useful for ranking in AI search.

Testing Shows Brand Mentions Are Important

Law went on to say that their data shows that brand mentions are important for ranking. He mentions a correlation coefficient of 0.67, which is a measure of how strongly two variables are related.

Here are the correlation coefficient scales:

  • 1.0 = perfect positive correlation (two things are related).
  • 0.0 = no correlation.
  • –1.0 = perfect negative correlation (for example, for every minute you drive the distance gets smaller, a negative correlation).

So, a correlation coefficient of 0.67 means that there’s a strong relationship in what’s observed.

Law explained:

“And we did indeed test this with a bit of research.

So we looked at these factors that correlate with the amount of times a brand appears in AI overviews, tested tons of different things, and by far the strongest correlation, very, very strong correlation, almost 0.67, was branded web mentions.

So if your brand is mentioned in a ton of different places on the web, that correlates very highly with your brand being mentioned in lots of AI conversations as well.”

He goes on to recommend identifying industry domains that tend to get cited in AI search for your topics and try to get mentioned on those websites.

Law also recommended getting mentions on user-generated content sites like Reddit and Quora. Next he recommended getting mentioned on review sites and on YouTube video in the transcripts because YouTube videos are highly cited by AI search.

Ahrefs Brand Radar Tool

Lastly, they discussed their Ahrefs tool called Brand Radar that’s useful for identifying domains that are frequently mentioned in AI search surfaces.

Law explained:

“And obviously, we have a tool that does exactly that. It actually helps you find the most commonly cited domains.  …if you put in whatever niche you’re interested in, you can see not only the top domains that get mentioned most often across all of the thousands, hundreds of thousands, millions of conversations we have indexed. You can also see the individual pages that get most commonly mentioned.

Obviously, if you can get your brand on those pages, yeah, immediately your AI visibility is going to shoot up in a pretty dramatic way.”

Citations Are The New Backlinks

Tim Soulo called citations the new backlinks for the AI search era and recommended their Brand Radar tool for identifying where to get mentions. In my opinion, getting a brand mentioned anywhere that’s relevant to your users or customers could also be helpful for ranking in the regular search  as well as AI (Read: Google’s Branded Search Patent)

Watch the Ahrefs podcast starting at about the 6:30 minute mark:

How to Win in AI Search (Real Data, No Hype)

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Nov 4 2025
Kinsta Managed WordPress Host Won’t Charge For Bot Traffic via @sejournal, @martinibuster

WordPress managed web hosting company Kinsta announced that it is changing how it bills its customers by not charging users for bandwidth related to unwanted bot and scraper traffic.

Daniel Pataki, CTO at Kinsta explained:

“In the past 12 months we’ve seen bot traffic rise due to the prevalence of both good and bad uses of AI. These bots can not be filtered as effectively, modifying our typical visits-to-bandwidth ratio. We’re working internally and with Cloudflare to improve bot filtering, but our top priority remains our customers’ success. Reducing bot-related costs as quickly as possible will have the greatest impact.”

Bot And Scraper Traffic Out Of Control

Anyone who’s watched their live traffic statistics can confirm that scraper and hacker bots make up a significant amount of traffic to a website, accounting for as much as half of the bandwidth costs for a website. I still remember the time I added a forum to a content site a few years ago and purposely left it without bot protection to see how long it would take to get spammed. I didn’t have to wait long; a spam bot registered itself and started posting spam within minutes.

Kinsta is providing bandwidth-based options that don’t charge for wasted bandwidth while also providing options such as caching and CDNs that help mitigate the impact of bad bot visits.

Kinsta’s announcement explains:

“Now with bandwidth-based options, Kinsta is giving customers more choice, transparency and control in how they pay for hosting: by visits or bandwidth. Customers are not locked into a single pricing model. This is consistent with Kinsta’s long-term approach of delivering quality and building trust. The new pricing option is setting the standard for hosting by giving customers the freedom to choose how they pay, in a way that reflects how the modern web actually works.”

The new feature is available to every visitor-based tier, enables the flexibility to switch between visits and bandwidth-based, and with improved usage notifications plus no charges for scrapers and bad bots the risk of unexpectedly running out of bandwidth is lower.

Read Kinsta’s announcement:

Kinsta Launches Bandwidth-Based Pricing to Give Website Owners and Developers More Hosting Control

Featured Image by Shutterstock/Paul shuang

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Nov 3 2025
Report: Apple To Lean On Google Gemini For Siri Overhaul via @sejournal, @MattGSouthern

Apple is reportedly paying Google to build a custom Gemini AI model that will power a major Siri upgrade targeted for spring 2026, according to Bloomberg’s Mark Gurman.

The custom Gemini model is expected to run on Apple’s Private Cloud Compute infrastructure. Neither Apple nor Google has officially announced the partnership.

What’s Being Reported

Bloomberg reports Apple conducted an internal evaluation comparing AI models from Google and Anthropic for the next-generation Siri.

Google’s Gemini won based largely on financial terms. Bloomberg says Anthropic’s Claude would have cost Apple more than $1.5 billion annually.

According to the report, Google’s models will provide the query planner and summarizer components of Siri’s new architecture. Apple’s own Foundation Models would continue handling on-device personal data processing, with the Google-supplied models running on Apple’s servers.

The project carries the internal codename “Glenwood.”

Apple Won’t Acknowledge Google’s Role

Bloomberg reports Apple plans to market the updated Siri as Apple technology running on Apple servers through an Apple interface, without promoting Google’s involvement.

In practice, Gemini would operate behind the scenes while Apple positions the capabilities as its own work.

Launch Timeline

Bloomberg reports Apple is targeting spring 2026 for the Siri overhaul as part of iOS 26.4.

Earlier Bloomberg reporting also pointed to a smart home display device on a similar timeline that could showcase the assistant’s expanded capabilities.

What We Don’t Know Yet

Financial terms beyond the broad “paying Google” characterization are undisclosed.

Neither company has confirmed the partnership, and the legal and technical data-handling arrangements are not public. It’s also unclear whether the deal is finalized or still being negotiated.

Why This Matters

A Gemini-powered backend could change how Siri answers questions, and who gets credit in AI responses, even if the branding remains Apple-only.

If Bloomberg’s report holds, more answers will start and finish inside Siri and Spotlight on iPhone, which can reduce early web discovery.

The open questions are how sources will appear and whether traffic will be traceable.

Looking Ahead

Apple has already enabled ChatGPT access within Siri and Writing Tools as part of Apple Intelligence, and Anthropic says Claude is available in Xcode 26 for developers.

The potential Gemini partnership would be Apple’s most consequential AI arrangement to date because it would underpin core Siri functionality rather than optional features.

Watch for official details closer to the iOS 26.4 window.

Featured Image: Thrive Studios ID/Shutterstock

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Nov 3 2025
GEO Platform Shutdown Sparks Industry Debate Over AI Search via @sejournal, @MattGSouthern

Benjamin Houy shut down Lorelight, a generative engine optimization (GEO) platform designed to track brand visibility in ChatGPT, Claude, and Perplexity, after concluding most brands don’t need a specialized tool for AI search visibility.

Houy writes that, after reviewing hundreds of AI answers, the brands mentioned most often share familiar traits: quality content, mentions in authoritative publications, strong reputation, and genuine expertise.

He claims:

“There’s no such thing as ‘GEO strategy’ or ‘AI optimization’ separate from brand building… The AI models are trained on the same content that builds your brand everywhere else.”

Houy explains in a blog post that customers liked Lorelight’s insights but often churned because the data didn’t change their tactics. In his view, users pursued the same fundamentals with or without GEO dashboards.

He argues GEO tracking makes more sense as one signal inside broader SEO suites rather than as a standalone product. He points to examples of traditional SEO platforms incorporating AI-style visibility signals into existing toolsets rather than creating a separate category.

Debate Snapshot: Voices On Both Sides

Reactions show a genuine split in how marketers see “AI search.”

Some SEO professionals applauded the back-to-basics message. Others countered with cases where assistant referrals appear meaningful.

Here are some of the responses published so far:

  • Lily Ray: “Thank you for being honest and for sharing this publicly. The industry needs to hear this loud and clear.”
  • Randall Choh: “I beg to differ. It’s a growing metric… LLM searches usually have better search intents that lead to higher conversions.”
  • Karl McCarthy: “You’re right that quality content + authoritative mentions + reputation is what works… That’s not a tool. It’s a network.”
  • Nikki Pilkington raised consumer-fairness questions about shuttering a product and whether prior GEO-promotional content should be updated or removed.

These perspectives capture the industry tension. Some see AI search as a new performance channel worth measuring. Others see the same brand signals driving outcomes across SEO, PR, and now AI assistants.

How “AI Search Visibility” Is Being Measured

Because assistants work differently from web search, measurement is still uneven.

Assistants surface brands in two main ways: by citing and linking sources directly in answers, and by guiding people into familiar web results.

Referral tracking can come through direct links, copy-and-paste, or branded search follow-ups.

Attribution is messy because not all assistants pass clear referrers. Teams often combine UTM tagging on shared links with branded-search lift, direct-traffic spikes, and assisted-conversion reports to triangulate “LLM influence.”

That patchwork makes case studies persuasive but hard to generalize.

Why This Matters

The main question is whether AI search needs its own optimization framework or if it primarily benefits from the same brand signals.

If Houy is correct, standalone GEO tools might only produce engaging dashboards that seldom influence strategy.

On the other hand, if the advocates are correct, overlooking assistant visibility could mean missing out on profitable opportunities between traditional search and LLM-referred traffic.

What’s Next

It’s likely that SEO platforms will continue to fold “AI visibility” into existing analytics rather than creating a separate category.

The safest path for businesses is to continue doing the brand-building work that assistants already reward, while testing assistant-specific measurements where they are most likely to pay off.

Featured Image: Roman Samborskyi/Shutterstock

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