Google AI Overviews Appear On 21% Of Searches: New Data via @sejournal, @MattGSouthern

Ahrefs analyzed 146 million search results to determine which query types trigger AI Overviews. The research tracked AIO appearance across 86 keyword characteristics.

Here’s a concise look at the patterns and how they may affect your strategy.

What The Analysis Found

AI Overviews appear on 20.5% of all keywords. Specific query types show notable variance, with some categories hitting 60% trigger rates while others stay below 2%.

Patterns Observed Across Query Types

Single-word queries activate AIOs only 9.5% of the time, whereas queries with seven or more words trigger them 46.4%. This correlation indicates that Google primarily uses AIOs for complex informational searches rather than simple lookups.

The question format also shows a similar trend: question-based queries result in AIOs 57.9% of the time, while non-question queries have a much lower rate of 15.5%.

The most significant distinctions are seen based on intent. Informational queries make up 99.9% of all AIO appearances, while navigational queries trigger AIOs just 0.09%. Commercial queries account for 4.3%, and transactional queries for 2.1%.

Patterns Observed Across Industry Categories

Science queries have an AIO rate of 43.6%, while health queries are at 43.0%, and pets & animals reach 36.8%. People & society questions result in AIOs 35.3% of the time.

In contrast, commerce categories exhibit opposite trends. Shopping queries are associated with AIOs only 3.2% of the time, the lowest in the dataset. Real estate remains at 5.8%, sports at 14.8%, and news at 15.1%.

YMYL queries display unexpectedly high trigger rates. Medical YMYL searches trigger AI Overviews 44.1% of the time, financial YMYL hits 22.9%, and safety YMYL reaches 31.0%.

These findings contradict Google’s focus on expert content for topics that could impact health, financial security, or safety.

Queries With Low Presence Of AI Overviews

6.3% of “very newsy” keywords trigger AI Overviews, while 20.7% of non-news queries display AIOs.

The pattern indicates that Google deliberately limits AIOs for time-sensitive content where accuracy and freshness are essential.

Local searches demonstrate a similar trend, with only 7.9% of local queries showing AI Overviews compared to 22.8% for non-local queries.

NSFW content consistently avoids AIOs across categories: adult queries trigger AIOs 1.5% of the time, gambling 1.4%, and violence 7.7%. Drug-related queries have the highest NSFW trigger rate at 12.6%, yet this remains well below the baseline.

Brand vs. Non-Brand

Branded keywords show slight differences compared to non-branded ones. Non-branded queries trigger AIOs 24.9% of the time, whereas branded queries do so 13.1% of the time.

The data indicates that AIOs occur 1.9 times more frequently for generic searches than for brand-specific lookups.

No Correlation With CPC

CPC shows no meaningful correlation with AIO appearance. Keyword cost-per-click values don’t affect trigger rates across any price range tested, with rates hovering between 12.4% and 27.6% regardless of commercial value.

Why This Matters

Publishers focused on informational content encounter the greatest AIO exposure. Question-based and how-to guides align closely with Google’s trigger criteria, putting educational content publishers at the highest risk of traffic loss.

Medical content has the highest category-specific AIO rate, despite concerns about AI accuracy in health advice.

Ecommerce and news publishers are relatively less affected by AIOs. The low trigger rates for shopping and news queries indicate these sectors experience less AI-driven traffic disruption compared to informational sites.

Looking Ahead

Using this data, publishers can review their current keyword portfolios to identify AIO exposure patterns. The most reliable indicators are query intent and length, with industry category and question format also playing significant roles.

AIO exposure varies considerably across different industry categories, with differences exceeding 40 percentage points between the highest and lowest. Content strategies need to consider this variation at the category level instead of assuming consistent baseline risk across all topics.

For a more in-depth examination of this data, see the full analysis.

Featured Image: Zorion Art Production/Shutterstock

Google Finance Gets AI Deep Search & Prediction Market Data via @sejournal, @MattGSouthern

Google Finance is rolling out Deep Search capabilities, prediction markets data, and enhanced earnings tracking features across its AI-powered platform.

The updates expand Google Finance beyond basic market data into multi-step research workflows and crowd-sourced probability forecasting. Google announced the changes today, with features rolling out over the coming weeks, starting with Labs users.

Deep Search For Financial Research

Deep Search handles complex financial queries by issuing up to hundreds of simultaneous searches and synthesizing information across multiple sources.

You can ask detailed questions and select the Deep Search option. Gemini models then generate fully cited comprehensive responses within minutes, displaying the research plan during generation.

Image Credit: Google

Robert Dunnette, Director of Product Management for Google Search, wrote:

“From there, our advanced Gemini models will get to work, issuing up to hundreds of simultaneous searches and reasoning across disparate pieces of information to produce a fully cited, comprehensive response in just a few minutes.”

Deep Search offers higher usage limits for Google AI Pro and AI Ultra subscribers. Users can access it through the Google Finance experiment in Labs.

Prediction Markets Integration

Google Finance is adding support for prediction markets data from Kalshi and Polymarket, with availability rolling out over the coming weeks, starting with Labs users.

You can query future market events directly from the search box to see current probabilities and historical trends.

An example query includes “What will GDP growth be for 2025?”

The feature rolls out this week to Labs users first.

Enhanced Earnings Tracking

Google launched earnings tracking features that provide live audio streams, real-time transcripts, and AI-generated insights during corporate earnings calls.

The Earnings tab shows scheduled calls, streams live audio during calls, and maintains transcripts for later reference. AI-powered insights under “At a glance” update before, during, and after calls with information from news reports and analyst reactions.

You can compare financial data against historical results, view performance versus expectations, and access earnings documents and SEC forms.

India Expansion

Google Finance begins rolling out in India this week with support for English and Hindi.

The India launch initially offers the core Google Finance experience. Deep Search, prediction markets, and earnings features launch first in the U.S. and will expand internationally over time.

Why This Matters

Deep Search reduces the time needed to gather financial data from multiple sources, potentially resulting in fewer webpage visits.

Prediction markets offer crowd-sourced probability estimates that complement analyst forecasts. Live earnings tracking integrates call audio, transcripts, and analyst reactions into a single interface during reporting season.

Looking Ahead

Deep Search and prediction markets roll out over the coming weeks, with Labs users getting early access. Google AI Pro and AI Ultra subscribers receive higher usage limits for Deep Search queries.

The India expansion marks Google Finance’s first international launch beyond the U.S. Access the beta at google.com/finance/beta while signed into a Google account.

Featured Image: Juan Alejandro Bernal/Shutterstock

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

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.