The World Wide Web Consortium (W3C) unveiled a new logo for the organization that is designed to transcend one language family and expresses abstract qualities like timelessness and reliability. The result is an abstract logo in the familiar blue and white colors, purposely designed to be evocative, to suggest but not concretely explain.

This evocative way of communication is called polysemy, where something can represent multiple related things, depending on the viewers personal experience and subjective interpretation. It’s a valid design choice for an organization that extends around the world and involves people with diverse backgrounds.

Transcending Language Family

The previous W3C logo emphasized the letters and numbers W3C. That works for English users but probably less so for users who speak other languages, especially those who use other kinds of letter scripts, and for people who are oriented to RTL (right-to-left) spelling.

The goal of creating a logo with a “style that transcends a single language family” makes sense for a global organization.

The W3C explains:

“We moved from using distinct letters and numerals in the logo to creating an abstract symbol to represent W3C. We chose a forward-looking style that transcends a single language family. This approach emphasizes W3C’s worldwide connection.”

What Does The Logo Symbol Mean?

What the symbol means requires multiple mixed metaphors. The explanation is that the circle depicts unity and forward motion. The symbol within the circle is a coil, which they explain is openly evocative of many things like a wave, a hand, or DNA. They also say that part of the coil is evocative of a heart.

Screenshot Of New W3C Logo

They essentially chose a symbol that does not represent anything but is evocative of whatever the individual sees in it.

Here’s how it’s explained:

“This circle depicts unity, constant motion, and moving forward. The symbol is a coil, inspired by the concepts of completion and progress reflected in our work. To some, the coil evokes waves — to others, a hand, or the spiral structure of a DNA helix. It has a curl that resembles a heart. This imagery communicates that W3C is the ‘DNA at the heart of the web’.”

See also: Google’s Mueller on Ranking Impact of Poor HTML

W3C Video About The Logo

There is a video that accompanies the logo that helps explain how the logo reflects the mission of the W3C as a global non-profit entity that champions ideals of accessibility, internationalization and so on. Like the logo, it expresses ideas in the form of concepts, expressed in a poetic style.

Part of it explains:

From the very beginning, from a single dot to a complex system, we are open, we are human, we are innovative, we are inclusive, we are for you, we are for everyone.
We champion accessibility.  We champion internationalization.  We champion privacy. We champion security.”

[embedded content]

What do you think? Does the new logo work for you?

Read more about the new logo at the W3C:
The World Wide Web Consortium (W3C) adopts a new logo to signal positive changes

https://www.searchenginejournal.com/w3c-rolls-out-a-new-evocative-logo/557430/

Perplexity released its Comet browser to everyone today, shifting from a waitlist to free desktop downloads worldwide.

Comet bakes an AI assistant into every new tab so you can ask questions, summarize pages, and navigate without jumping between search results and multiple tools.

Perplexity first introduced Comet in July in a limited release. Since then, the company says “millions” have joined the waitlist, and early users asked 6–18 times more questions on day one.

The move poses a challenge to traditional search engines and browsers by adopting an AI-first approach to web navigation, which reduces the need for multiple searches and the management of numerous tabs.

What Makes Comet Different

At the core of Comet’s functionality is the Comet Assistant, an AI-powered helper that browses alongside users and handles tasks such as research, meeting support, coding assistance, and e-commerce activities.

The assistant appears in every new tab, ready to answer questions or complete actions without requiring users to navigate away from their current workflow.

Unlike traditional browsers where users must open a separate search engine, copy information between tabs, or use multiple tools, Comet integrates assistance directly into the browsing experience. You can ask questions in natural language, and the assistant provides answers drawn from web sources.

Background Assistants

Perplexity also announced Background Assistants today. These assistants work simultaneously and asynchronously in the background, handling tasks without requiring active user supervision.

The Background Assistants join the recently announced Email Assistant, currently available to Max Subscribers. The Email Assistant can be cc’d on email threads to handle scheduling, draft replies, and manage inbox tasks without opening a separate application.

Mobile & Voice Coming Soon

While Comet has been desktop-only since its July launch, Perplexity recently previewed mobile versions for iPhone and Android.

The mobile version will include voice technology, allowing users to interact with Comet assistants through speech rather than typing.

Availability

Comet is now available for free download at perplexity.ai/comet for desktop users.

For tips on using the browser, see Perplexity’s resource hub.

Featured Image: Sidney van den Boogaard/Shutterstock

https://www.searchenginejournal.com/perplexity-launches-comet-browser-for-free-worldwide/557432/

Branding has never been more important. Online audiences continue to yearn for connection, and a strong brand identity can bridge the gap.

Katie Morton, Editor-in-Chief of Search Engine Journal, sits down with Mordy Oberstein, Founder of Unify Brand Marketing, to discuss why authenticity in branding and online content matters more than ever. They also discuss the need for genuine cross-functional collaboration.

For marketers rethinking how brand identity fits into their strategies, you may find this conversation insightful. It’s filled with practical tips and takeaways from the State of SEO: How to Survive report.

Watch the video or read the full transcript below.

[embedded content]

Editor’s note: The following transcript has been edited for clarity, brevity, and adherence to our editorial guidelines.

Katie Morton: Hey, everybody. It’s Katie Morton, Editor-in-Chief of Search Engine Journal, and I’m sitting down today with Mordy Oberstein. Mordy, go ahead and introduce yourself.

Mordy Oberstein: I’m Mordy. I’m the founder of Unify Brand Marketing. I work on brand development, fractional marketing, and marketing strategy. But my main focus is brand development and how to integrate that into your actual marketing activities and your actual strategy.

Katie: Which is just becoming so crucial these days, especially with all of the changes we’ve seen over the last few years. Branding: I don’t want to say it’s everything, but it’s definitely up there.

Mordy: Quite the topic in the performance space, suddenly.

Katie: Yeah, I’m going to say more than ever, really.

Mordy: Which is kind of what we’re here to talk about.

Katie: We are also going to talk about branding within the scope of the State of SEO overall.

Branding And The State Of SEO

Katie: Every year, Search Engine Journal puts out a survey about the state of SEO. We ask questions to try and get our finger on the pulse of what people are doing. This year, we did a SWOT analysis: strengths, weaknesses, opportunities, and threats,  to see how everybody’s doing and how they’re dealing with it.

The subtitle of this year’s ebook is How to Survive. And I would say, arguably, branding is one of those keys to survival.

Mordy: Yeah. And it keeps popping up. It came up in the survey a bunch of times. One of the questions was, “What are your most improved outcomes?” and 34.8% of people surveyed said brand visibility increased.

They were able to increase their brand visibility in search engines. And you can see it’s become way more of a focus.

One of the comments you pulled was from John Shehata, who’s brilliant. And his quote was: “Double down on experience. It’s the first E in E-E-A-T.”

For those unfamiliar, E-E-A-T stands for experience, expertise, authoritativeness, and trustworthiness, which are part of Google’s quality rater guidelines. And what John said that really resonated with me was: “Authenticity builds trust, both with users and AI systems.”

That got me thinking about this whole brand conversation. Because you keep hearing brand, brand, brand. You see it in the survey results, John’s talking about it here. But my question is: how do you do that? How do you actually build authenticity?

I agree with John a million percent – you need authenticity. And people are clearly seeing the value in brand all of a sudden, which is great. Super happy about it.

For performance marketers, though, it’s definitely a different way of thinking, a different way of operating. And one of the things SEOs especially need to be conscious of, and maybe push through, is the old verbiage.

Verbiage is a real thing. Carolyn Shelby actually wrote an article on SEJ about this whole SEO vs. GEO and the “words matter” thing. And there were so many stats in the survey about E-E-A-T and building E-E-A-T.

Part of the problem is thinking about it as “E-E-A-T.” Because that’s the context of SEO, the context of trying to deal with an algorithm. But when you’re trying to build authenticity, that’s not really the context you’re working in.

Building real authenticity does translate into building search equity with algorithms. I don’t think they’re different things. But authenticity itself comes from knowing yourself, being in touch with your brand identity, having a very focused brand identity, and having one that’s actually true to yourself.

I was talking to, I think it was a client, maybe a potential client, and I said, “You know, you could do X, you could do Y. Y is not who you are and it won’t work no matter no matter how hard you want to work so do X because X is much more in line with who you are. ”

Authenticity Beyond Acronyms

Mordy: Having the ability to understand who you are and make authentic decisions from there builds authenticity.

So if you’re stuck using old acronyms, thinking about it from an algorithm point of view and not from an actual who are we, how do we showcase ourselves, how do we transmit value to our audience, and you can’t get beyond the acronyms, I think you’re going to have a little bit of a hard time.

Katie: Yeah, Mordy and I were talking about this offline, this concept of the human element, as opposed to the framing SEOs used to go for.

And we’d really like to move the vocabulary forward and away from E-E-A-T. As Mordy said, it’s very algorithm-focused, and that in itself is kind of inauthentic. It’s machine-focused instead of looking at human morals and values, and what makes us human, and what makes us appeal to one another.

And in a previous episode, we talked about those emotional connections: who you really are, and who you’re most gifted to serve. As opposed to just trying to build this concept of E-E-A-T that’s based on these rater guidelines.

Mordy: Sounds like R-A-I-D-E-R. Rater. It’s interesting because that’s what, if you want to put it in marketing terms, we’re really talking about: your ability to resonate.

And you can only resonate when you’re actually your authentic self. Imagine you went out there and did something that wasn’t really in line with who you are. People would pick up on that. It wouldn’t actually resonate.

So to create authenticity, you have to be authentic. And in order to be authentic, you have to know, well, who the heck are we, so that we can actually be ourselves, right?

It sounds easy, but it’s very complicated. Because there are a lot of mitigating factors that come in. You try to pigeonhole things. You want to get your messaging super catchy. There are a lot of things that make it complicated.

But at its core, if you look at it at a micro level, it’s not complicated.

Where it gets complicated is another statistic I wanted to address, your eighth question in the survey. That one was about structural changes within the organization.

And one of the replies was: cross-functional collaboration increased. Thirty-seven point seven percent of respondents said, “We started to focus on cross-functional operations.”

Which is, yay. Yes. Because leaving SEO aside, LLMs, visibility, rankings, performance, etc., that’s just how your organization should function in a healthy way. It’s good, inherently, for your organization to move forward.

But from an SEO/LLM point of view, if you’re not synced up, if you’re siloed, that’s a problem. Coming from a background in enterprise, where everything is very siloed, I can tell you: if you’re siloed, you can’t be consistent.

You can have one team writing one set of content, the LLM picking it up, and another team writing a different set of content, positioning the brand differently.

This is what I really want to get into. Often, teams don’t understand the same brand the same way.

Katie: And yeah, that creates this fractured, disjointed presentation out there in the world. It makes it harder for people to understand what you’re about.

Why Vision And Meaning Matter

Mordy: Those are for people, and in turn, it makes it harder for algorithms, LLMs, and all the machines.

If you’re telling me one thing, and then I ask somebody else on your team about you and they give me a different answer – well, I’m confused. Color me confused. And that’s because it is confusing.

And it happens a lot. More often than you would think. And the reason why it happens, I want to diagnose it, ninety-nine point nine, nine, nine, nine, nine percent of the time, the reason this happens is there’s a lack of confidence and actual vision coming down from up top.

That definition or vision of who we are, what we want to do, who we’re serving, why we’re doing it, what we’re trying to achieve, and why that’s meaningful, that has to be clear.

Because if you’re just telling your team internally, ‘We want to hit this KPI, we need seventy-five percent growth, and we need to achieve X metric,’ that doesn’t get people bought in.

What gets people bought in is knowing you’re trying to do something meaningful. You’re a cohesive group of people, individuals coming together in an organization, working toward one set thing.

People aren’t machines. They need something meaningful to attach to, just like your audience needs something meaningful in order to perceive you, connect with you, and resonate with you.

Fast-Moving SEO & The Need For Real Communication

So, the people who work for you? They’re your audience, too. And if you don’t have something clear, distinct, and meaningful that they can grab onto, you end up fractured situation. One team understands it one way. The head of marketing, another way. The head of social media, another way. The head of SEO, another way. And then, without realizing it, you’re completely siloed.

I think it’s one of the things I’d really like to see more of. I’m glad the survey touched on it, but I’d like to see more conversation around un-siloing your marketing teams. I don’t think that internal comms conversation is happening enough yet. And we need it.

Katie: Absolutely. And I’ll also say another landmine in all of this is how fast everything moves these days.

For example, before we got on here, we were talking about certain points that come up in SEO. Things change so quickly. If something’s untested, different people can have different ideas or opinions about how it works.

So it’s not always just a top-down failure of leadership. Sometimes it’s simply that things are moving so fast. One team thinks one thing, another team thinks another, and they both put out mixed messages before anyone has even realized there’s a disconnect.

SEO and marketing can be as much art as science. Sometimes you need testing to bear things out over time. But in the interim, it’s like the Wild West of opinions. It’s hard to rein that in.

And it’s hard not to put out absolutes before something has been proven one way or another. And even then, it can change.

Mordy: What’s true for one website or brand might not be true for another, depending on their context.

So yeah, it’s hard now. Because you’re right. You hear different things from different places on the outside, you try to assimilate, and one team might latch onto one piece of advice while another acts on something else.

And then you end up with this idea of communication, but really it’s not. Teams say we have a monthly sync; our social team meets with the blog team to have a monthly sync…that’s not actually communicating. I know it feels like it is, but you need something a little bit different than that.

Katie: Yeah, I would say the real fluidity of communication between teams, whether that’s Slack or, you know, some people, [I’m] not a fan of the daily standup, but sometimes that can be helpful depending on the situation.

Mordy: By the way, it’s okay to get onto a daily standup and say, “I’ve got nothing new today.” That’s fine. “Okay, see you tomorrow.”

Katie: Right, right.

Mordy: That’s actually a valuable use of your time.

Final Thoughts

Katie: Yeah. It can be tough at Search Engine Journal, we’re very global. We have people across nearly every time zone. So a daily standup would be nearly impossible to accommodate. But we’re all on Slack all day, every day, and night. So the communication never stops.

Anyway, people need to figure out what works best for their team. But it’s definitely key these days, moving forward in SEO, and how to survive.

Mordy: Oh, and by the way, check out all the stats. I only picked those two, but there are tons more in there. So if you’re wondering, “Is that it?” No, there are a lot more. Those were just the two I harped on.

Katie: So, go to searchenginejournal.com/state-of-seo and you’ll see our latest ebook: State of SEO: How to Survive. Go ahead and click, sign up, and grab that.

And Mordy, what would you like to plug today?

Mordy: unifybrandmarketing.com.

Katie: Yes, book a consult with Mordy.

Alright. Thank you so much for sitting down with me today, Mordy. Always a pleasure.

Mordy: Yeah.

Katie: And we’ll catch you all next time. Bye.

Mordy: Bye.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/moving-beyond-e-e-a-t-branding-survival-and-the-state-of-seo/556361/

This week’s Ask An SEO question comes from a reader facing a common challenge when setting up international websites:

“I’m expanding into international markets but I’m confused about hreflang implementation. My rankings are inconsistent across different countries, and I think users are seeing the wrong language versions. What are the most common hreflang mistakes, and how do I audit my international setup?”

This is a great question and an important one for anyone working on websites that cover multiple countries or languages.

The hreflang tag is an HTML attribute that is used to indicate to search engines what language and/or geographical targeting your webpages are intended for. It’s useful for websites that have multiple versions of a page for different languages or regions.

For example, you may have a page dedicated to selling a product to a U.S. audience, and a different one about the same product targeted at a UK audience. Although both these pages would be in English, they may have differences in the terminology used, pricing, and delivery options.

It would be important for the search engines to show the U.S. page in the SERPs for audiences in the US, and the UK page to audiences in the UK. The hreflang tag is used to help the search engines understand the international targeting of those pages.

How To Use An Hreflang Tag

The hreflang tag comprises the “rel=” alternate code, which indicates the page is part of a set of alternates. The “href=” attribute, which tells the search engines the original page, and the “hreflang=” attribute, which details the country and or language the page is targeted to.

It’s important to remember that hreflang tags should be:

• Self-referencing: Each page that has an hreflang tag should also include a reference to itself as part of the hreflang implementation.
• Bi-directional: Each page that has an hreflang tag on it should also be included in the hreflang tags of the pages it references, so Page A references itself and Page B, with Page B referencing itself and Page A.
• Set up in either the XML sitemaps of the sites, or HTML/HTTP headers of the pages: Make sure that you are not only formatting your hreflang tags correctly, but placing them in the code where the search engines will look for them. This means putting them in your XML sitemaps, or in your HTML head (or in the HTTP header of documents like PDFs).

An example of hreflang implementation for the U.S. product page mentioned above would look like:

<link rel="alternate" href="https://yourstore.com/us/product" hreflang="en-us" />
<link rel="alternate" href="https://yourstore.com/uk/product" hreflang="en-gb" />
<link rel="canonical" href="https://yourstore.com/us/product" />

<link rel="alternate" href="https://yourstore.com/us/product" hreflang="en-us" />
<link rel="alternate" href="https://yourstore.com/uk/product" hreflang="en-gb" />
<link rel="canonical" href="https://yourstore.com/uk/product" />

Each page includes a self-referencing canonical tag, which hints to search engines that this is the right URL to index for its specific region.

Common Mistakes

Although in theory, hreflang tags should be simple to set up, they are also easy to get wrong. It’s also important to remember that hreflang tags are considered hints, not directives. They are one signal, among several, that helps the search engines determine the relevance of the page to a particular geographic audience.

Don’t forget to make hreflang tags work well for your site; your site also needs to adhere to the basics of internationalization.

Missing Or Incorrect Return Tags

A common issue that can be seen with hreflang tags is that they are not formatted to reference the other pages that are, in turn, referencing them. That means, Page A needs to reference itself and Pages B and C, but Pages B and C need to reference themselves and each other as well as Page A.

As an example the code above shows, if we were to miss the required return tag on the UK page, that points back to the U.S. version.

Invalid Language And Country Codes

Another problem that you may see when auditing your hreflang tag setup is that the country code or language code (in ISO 3166-1 Alpha 2 format) or language code (in ISO 639-1 format) isn’t valid. This means that either a code has been misspelled, like “en-uk” instead of the correct “en-gb,” to indicate the page is targeted towards English speakers in the United Kingdom.

Hreflang Tags Conflict With Other Directives Or Commands

This issue arises when the hreflang tags contradict the canonical tags, noindex tags, or link to non-200 URLs. So, for example, on an English page for a U.S. audience, the hreflang tag might reference itself and the English UK page, but the canonical tag doesn’t point to itself; instead, it points to the English UK page. Alternatively, it might be that the English UK page doesn’t actually resolve to a 200 status URL, and instead is a 404 page. This can cause confusion for the search engines as the tags indicate conflicting information.

Similarly, if the hreflang tag includes URLs that contain a no-index tag, you will confuse the search engines more. They will disregard the hreflang tag link to that page as the no-index tag is a hard-and-fast rule the search engines will respect, whereas the hreflang tag is a suggestion. That means the search engines will respect the noindex tag over the hreflang tag.

Not Including All Language Variants

A further issue may be that there are several pages that are alternatives to the one page, but it does not include all of them within the hreflang tag. By doing that, it does not signify that these other alternative pages should be considered a part of the hreflang set.

Incorrect Use Of “x-default”

The “x-default” is a special hreflang value that tells the search engines that this page is the default version to show when no specific language or region match is appropriate. This x-default page should be a page that is relevant to any user who is not better served by one of the other alternate pages. It is not a required part of the hreflang tag, but if it is used, it should be used correctly. That means making a page that serves as a “catch-all” page the x-default, not a highly localized page. The other rules of hreflang tags also apply here – the x-default URL should be the canonical of itself and should serve a 200 server response.

Conflicting Formats

Although it is perfectly fine to put hreflang tags in either the XML sitemap or in the head of a page, it can cause problems if they are in both locations and conflict with each other. It is a lot simpler to debug hreflang tag issues if they are only present in either the XML sitemap or in the head. It will also confuse the search engines if they are not consistent with each other.

The Issues May Not Just Be With The Hreflang Tags

The key to ensuring the search engines truly understand the intent behind your hreflang tags is that you need to make sure the structure of your website is reflective of them. This means keeping the internationalization signals consistent throughout your site.

Site Structure Doesn’t Make Sense

When internationalizing your website, whether you decide to use sub-folders, sub-domains, or separate websites for each geography or language, make sure you keep it consistent. It can help your users understand your site, but also makes it simpler for the search engines to decode.

Language Is Translated On-the-Fly Client-Side

A not-so-common, but very problematic issue with internationalization can be when pages are automatically translated. For example, when JavaScript swaps out the original text on page load with a translated version, there is a risk that the search engines may not be able to read the translated language and may only see the original language.

It all depends on the mechanism used to render the website. When client-side rendering uses a framework like React.js, it’s best practice to have translated content (alongside hreflang and canonical tags) available in the DOM of the page on first load of the site to make sure the search engines can definitely read it.

Read: Rehydration For Client-Side Or Server-Side Rendering

Webpages Are In Mixed Languages Or Poorly Translated

Sometimes there may be an issue with the translations on the site, which can mean only part of the page is translated. This is common in set-ups where the website is translated automatically. Depending on the method used to translate pages, you may find that the main content is translated, but the supplementary information, like menu labels and footers, is not translated. This can be a poor user experience and also means the search engines may consider the page to be less relevant to the target audience than pages that have been translated fully.

Similarly, if the quality of the translations is poor, then your audience may favor well-translated alternatives above your page.

Auditing International Setup

There are several ways to audit the international setup of your website, and hreflang tags in particular.

Check Google Analytics

Start by checking Google Analytics to see if users from other countries are landing on the wrong localized pages. For example, if you have a UK English page and a U.S. English page but find users from both locations are only visiting the U.S. page, you may have an issue. Use Google Search Console to see if users from the UK are being shown the UK page, or if they are only being shown the U.S. page. This will help you identify if you may have an issue with your internationalization.

Validate Tags On Key Pages Across The Whole Set

Take a sample of your key pages and check a few of the alternate pages in each set. Make sure the hreflang tags are set up correctly, that they are self-referencing, and also reference each of the alternate pages. Ensure that any URLs referenced in the hreflang tags are live URLs and are the canonicals of any set.

Review XML Sitemap

Check your XML sitemaps to see if they contain hreflang tag references. If they do, identify if you also have references within the <head> of the page. Spot check to see if these references agree with each other or have any differences. If there are differences in the XML sitemap’s hreflang tags with the same page’s hreflang tag in the <head>, then you will have problems.

Use Hreflang Testing Tools

There are ways to automate the testing of your hreflang tags. You can use crawling tools, which will likely highlight any issues with the setup of the hreflang tags. Once you have identified there are pages with hreflang tag issues, you can run them through dedicated hreflang checkers like Dentsu’s hreflang Tags Testing Tool or Dan Taylor and SALT Agency’s hreflangtagchecker.

Getting It Right

It is really important to get hreflang tags right on your site to avoid the search engines being confused over which version of a page to show to users in the SERPs. Users respond well to localized content, and getting the international setup of your website is key.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/ask-an-seo-what-are-the-most-common-hreflang-mistakes/556455/

Yoast announced the release of their Brand Insights tool, which helps track and monitor brand sentiment and visibility in AI platforms like ChatGPT. The new tool, currently in beta, is a new direction for Yoast because it’s not a plugin and doesn’t need CMS access. The complete tool is called Yoast SEO AI+.

The tool offers sentiment-tracking analysis by keywords, competitor rank benchmarking, citation analysis, and the ability to monitor specific brand questions.

The citation analysis is interesting because it tracks brand mentions. The sentiment analysis is also useful because it shows a graph based on keywords broken down by positive and negative sentiment.

Niko Körner, Senior Director of Product at Yoast explained:

“With Yoast AI Brand Insights, our customers can not only track their brand’s visibility, sentiment, and credibility in AI platforms like ChatGPT, but also see how they compare against the competition. As AI answers become a new starting point for customer journeys, this competitive perspective is crucial to staying ahead.

We worked hard to create a simplified KPI that truly reflects brand performance in the age of AI. Our AI Visibility Index combines sentiment, rank in LLM answers, brand mentions, and citations into one clear metric.

Soon, we will also be launching actionable recommendations to help businesses improve their AI visibility. This launch is only the beginning, and we are already working on improvements and expanding support for more large language models.”

The new Yoast tool is modestly priced, a sign that  Yoast is focusing on providing SEO tools for SMBs  who are interested in getting ahead in AI search.

Read more here:
Find out how your brand shows up in ai answers – Yoast SEO AI+

Featured Image by Shutterstock/Xharites

https://www.searchenginejournal.com/yoast-announces-new-ai-visibility-tool/557378/

OpenAI released the largest study to date on how users really use ChatGPT. I have painstakingly synthesized the ones you and I should pay heed to, so you don’t have to wade through the plethora of useful and pointless insights.

TL;DR

1. LLMs are not replacing search. But they are shifting how people access and consume information.
2. Asking (49%) and Doing (40%) queries dominate the market and are increasing in quality.
3. The top three use cases – Practical Guidance, Seeking Information, and Writing – account for 80% of all conversations.
4. Publishers need to build linkable assets that add value. It can’t just be about chasing traffic from articles anymore.

Image Credit: Harry Clarkson-Bennett

Chatbot 101

A chatbot is a statistical model trained to generate a text response given some text input. Monkey see, monkey do.

The more advanced chatbots have a two or more-stage training process. In stage one (less colloquially known as “pre-training”), LLMs are trained to predict the next word in a string.

Like the world’s best accountant, they are both predictable and boring. And that’s not necessarily a bad thing. I want my chefs fat, my pilots sober, and my money men so boring they’re next in line to lead the Green Party.

Stage two is where things get a little fancier. In the “post-training” phase, models are trained to generate “quality” responses to a prompt. They are fine-tuned on different strategies, like reinforcement learning, to help grade responses.

Over time, the LLMs, like Pavlov’s dog, are either rewarded or reprimanded based on the quality of their responses.

In phase one, the model “understands” (definitely in inverted commas) a latent representation of the world. In phase two, its knowledge is honed to generate the best quality response.

Without temperature settings, LLMs will generate exactly the same response time after time, as long as the training process is the same.

Higher temperatures (closer to 1.0) increase randomness and creativity. Lower temperatures (closer to 0) make the model(s) far more predictive and precise.

So, your use case determines the appropriate temperature settings. Coding should be set closer to zero. Creative, more content-focused tasks should be closer to one.

I have already talked about this in my article on how to build a brand post AI. But I highly recommend reading this very good guide on how temperature scales work with LLMs and how they impact the user base.

What Does The Data Tell Us?

That LLMs are not a direct replacement for search. Not even that close IMO. This Semrush study highlighted that LLM super users increased the amount of traditional searches they were doing. The expansion theory seems to hold true.

But they have brought on a fundamental shift in how people access and interact with information. Conversational interfaces have incredible value. Particularly in a workplace format.

Who knew we were so lazy?

1. Guidance, Seeking Information, And Writing Dominate

These top three use cases account for 80% of all human-robot conversations. Practical guidance, seeking information, and please help me write something bland and lacking any kind of passion or insight, wondrous robot.

I will concede that the majority of Writing queries are for editing existing work. Still. If I read something written by AI, I will feel duped. And deception is not an attractive quality.

2. Non-Work-Related Usage Is Increasing

• Non-work-related messages grew from 53% of all usage to more than 70% by July 2025.
• LLMs have become habitual. Particularly when it comes to helping us make the right decisions. Both in and out of work.

3. Writing Is The Most Common Workplace Application

• Writing is the most common work use case, accounting for 40% of work-related messages on average in June 2025.
• About two-thirds of all Writing messages are requests to modify existing user text rather than create new text from scratch.

I know enough people that just use LLMs to help them write better emails. I almost feel sorry for the tech bros that the primary use cases for these tools are so lacking in creativity.

4. Less So Coding

• Computer coding queries are a relatively small share, at only 4.2% of all messages.*
• This feels very counterintuitive, but specialist bots like Claude or tools like Lovable are better alternatives.
• This is a point of note. Specialist LLM usage will grow and will likely dominate specific industries because they will be able to develop better quality outputs. The specialized stage two style training makes for a far superior product.

*Compared to 33% of work-related Claude conversations.

It’s important to note that other studies have some very different takes on what people use LLMs for. So this isn’t as cut and dry as we think. I’m sure things will continue to change.

5. Men No Longer Dominate

• Early adopters were disproportionately male (around 80% with typically masculine names).
• That number declined to 48% by June 2025, with active users now slightly more likely to have typically feminine names.

Sure, us men have our flaws. Throughout history maybe we’ve been a tad quick to battle and a little dominating. But good to see parity.

• 89% of all queries are Asking and Doing related.
• 49% Asking and 40% Doing, with just 11% for Expressing.
• Asking messages have grown faster than Doing messages over the last year, and are rated higher quality.

A ChatGPT-built table with examples of each query type – Asking, Doing, and Expressing (Image Credit: Harry Clarkson-Bennett)

7. Relationships And Personal Reflection Are Not Prominent

• There have been a number of studies that state that LLMs have become personal therapists for people (see above).
• However, relationships and personal reflection only account for 1.9% of total messages according to OpenAI.

8. The Bloody Youth (*Shakes Fist*)

Takeaways

I don’t think LLMs are a disaster for publishers. Sure, they don’t send any referral traffic and have started to remove citations outside of paid users (classic). But none of these tech-heads are going to give us anything.

It’s a race to the moon, and we’re the dog they sent on the test flight.

But if you’re a publisher with an opinion, an audience, and – hopefully – some brand depth and assets to hand, you’ll be ok. Although their crawling behavior is getting out of hand.

Shit-quality traffic and not a lot of it (Image Credit: Harry Clarkson-Bennett)

One of the most practical outcomes we as publishers can take from this data is the apparent change in intents. For eons, we’ve been lumbered with navigational, informational, commercial, and transactional.

Now we have Doing. Or Generating. And it’s huge.

Even simple tools can still drive fantastic traffic and revenue (Image Credit: Harry Clarkson-Bennett)

SEO isn’t dead for publishers. But we do need to do more than just keep publishing content. There’s a lot to be said for espousing the values of AI, while keeping it at arm’s length.

Think BBC Verify. Content that can’t be synthesized by machines because it adds so much value. Tools and linkable assets. Real opinions from experts pushed to the fore.

But it’s hard to scale that quality. Programmatic SEO can drive amazing value. As can tools. Tools that answer users’ “Doing” queries time after time. We have to build things that add value outside of the existing corpus.

And if your audience is generally younger and more trusting, you’re going to have to lean into this more.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: Roman Samborskyi/Shutterstock

https://www.searchenginejournal.com/how-people-really-use-llms-and-what-that-means-for-publishers/556977/

Before we jump in:

• I hate to brag, but I will say I’m extremely proud to have placed 4th in the G50 SEO World Championships this past week.
• I’m speaking at NESS, the global News & Editorial SEO Summit, on October 22. Growth Memo readers get 20% off when code “kevin2025”

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Historically, backlinks have always been one of the most reliable currencies of visibility in search results.

We know links matter for visibility in AI-based search, but how they work inside LLMs – including AI Overviews, Gemini, or ChatGPT & Co.- is still somewhat of a black box.

The rise of AI search models changes the rules of organic visibility and the competition for share of voice in LLM results.

So the question is, do backlinks still earn visibility in AI-based modalities of search… and if so, which ones?

If backlinks were the currency of the pre-LLM web, this week’s analysis is a first look at whether they’re still legal tender in the new AI search economy.

Together with Semrush, I analyzed 1,000 domains and their AI mentions against core backlink metrics.

Image Credit: Kevin Indig

The data surfaced four clear takeaways:

1. Backlink-earned authority helps, but it’s not everything.
2. Link quality outweighs volume.
3. Most surprisingly, nofollow links pull real weight.
4. Image links can move the needle on authority.

These findings help us all understand how AI models surface sites, along with exposing what backlink levers marketers can pull to influence visibility.

Below, you’ll find the methodology, deeper data takeaways, and, for premium subscribers, recommendations (with benchmarks) to put these findings into action.

Methodology

For this analysis, I looked at relationships between AI mentions for 1,000 randomly selected web domains. All data is from the Semrush AI SEO Toolkit, Semrush’s AI visibility & search analytics platform.

Along with the Semrush team, I examined the number of mentions across:

• ChatGPT.
• ChatGPT with Search activated.
• Gemini.
• Google’s AI Overviews.
• Perplexity.

(If you’re wondering where Claude.ai fits in this analysis, we didn’t include it at this time as its user base is generally less focused on web search and more on generative tasks.)

For the platforms above, we measured Share of Voice and the number of AI mentions against the following backlink metrics:

• Total backlinks.
• Unique linking domains.
• Follow links.
• Nofollow links.
• Authority Score (a Semrush metric referred to as Ascore below).
• Text links.
• Image links.

In this analysis, I used two different ways of measuring correlation across the data: a Pearson correlation and a Spearman correlation.

If you are familiar with these concepts, skip to the next section where we dive into the results.

For everyone else, I’ll break these down so you have a better understanding of the findings below.

Both Pearson and Spearman are correlation coefficients – numbers between -1 and +1 that measure how strongly two different variables are related.

The closer the coefficient is to +1 or -1, the more likely and stronger the correlation. (Near 0 means weak or no correlation at all.)

• Pearson’s r measures the strength and direction of a linear relationship between two variables. Pearson looks at a linear correlation across the data using the raw values. This way of measuring is sensitive to outliers. But, if the relationship curves or has thresholds, Pearson under-measures it.
• Spearman’s ρ (rho) measures the strength and direction of a monotonic relationship, or whether values consistently move in the same or opposite direction, not necessarily in a straight line. Spearman looks at rank correlation across the data. It asks whether higher X tends to come with higher Y; Spearman correlation asks: “When one thing increases, does the other usually increase too?”. It’s a correlation that is more robust to outliers and accounts for non-linear, monotonic patterns.

A gap between Pearson and Spearman correlation coefficients can mean the gains are non-linear.

In other words: There’s a threshold to cross. And that means the effect of X on Y doesn’t kick in right away.

Examining both the Pearson and Spearman coefficients can tell us if nothing (or very little) happens until you pass a certain point – and then once you exceed that point, the relationship shows up strongly.

Here’s a quick example of what an analysis that involves both coefficients can reveal:

Spending $500 (action X) on ads might not move the needle on sales growth (outcome Y). But once you cross, say, $5,000/month (action X), sales start growing steadily (outcome Y).

And that’s the end of your statistics lesson for today.

Image Credit: Kevin Indig

The first signal we examined was the strength of the relationship between the number of backlinks a site gets versus its AI Share of Voice.

Here’s what the data showed:

• Authority Score has a moderate link to Share of Voice (SoV): Pearson ~0.23, Spearman ~0.36.
• Higher authority means higher SoV, but the gains are uneven. There’s a threshold you need to cross.
• Authority supports visibility, yet it does not explain most of the variance. What this means is that backlinks do have an impact on AI visibility, but there is more to the story, like your content, brand perceptions, etc.

Also, the number of unique linking domains matters more than the total number of backlinks.

In plain terms, your site is more likely to have a larger SoV when you have links from many different websites than a huge number of links from just a few sites.

Image Credit: Kevin Indig

Across all models, the strongest relationship occurred between Authority Score (0.65 Pearson, 0.57 Spearman) and the number of mentions

Here’s how Semrush defines the Authority Score measurement:

Authority Score is our compound metric that grades the overall quality of a website or a webpage. The higher the score, the more assumed weight a domain’s or webpage’s outbound links to another site could have.

It takes into account the number and quality of backlinks, organic traffic to link source pages, and the spamminess of the link profile.

Of course, Ascore is just a proxy for quality. LLMs have their own way of arriving at backlink quality. But the data shows that we can use Semrush’s Ascore as a good representative.

Most models value this metric equally for mentions, but ChatGPT Search and Perplexity value it the least compared to the average.

Surprisingly, regular ChatGPT (without search activated) weighs Ascore the most out of all models.

Critical to know: Median mentions jump from ~21.5 in decile 8 to ~79.0 in decile 9. The relationship is non-linear. In other words, the biggest gains come when you hit the upper boundaries of authority, or Ascore in this case.

(For context, a decile is a way of splitting a dataset into 10 equal parts. Each segment, or decile, contains 10% of the data points when they’re sorted in order.)

Image Credit: Kevin Indig

Perhaps the most significant finding from this analysis is that it doesn’t matter much if the links are set to nofollow or not!

And this has huge implications.

Confirmation of the value of nofollow links is so important because these types of links tend to be easier to build than follow links.

This is where LLMs are distinctly different from search engines: We’ve known for a while that Google also counts nofollow links, but not how much and for what (crawling, ranking, etc).

Once again, you won’t see big gains until you’re in the top 3 deciles, or the top 30% of the data points.

Follow links → Mentions:

• Pearson 0.334, Spearman 0.504

Nofollow links → Mentions:

• Pearson 0.340, Spearman 0.509

Conversely, Google’s AI Overviews and Perplexity weighed regular links the highest and nofollow links the least.

And interestingly, Gemini and ChatGPT weigh nofollow links the highest (over regular follow links).

Here’s my own theory as to why Gemini and ChatGPT weigh nofollow more:

With Gemini, I’m curious if Google weighs nofollow links higher than we have believed them to be in the past. And with ChatGPT, my hypothesis is that Bing is also weighing nofollow links higher (once Google started doing it, too). But this is just a theory, and I don’t have the data to support it at this time.

Image Credit: Kevin Indig

Beyond text-based backlinks, we also tested if image-based backlinks carry the same weight.

And in some cases, they had a stronger relationship to mentions than text-based links.

But how strong?

• Images vs mentions: Pearson 0.415, Spearman 0.538
• Text links vs mentions: Pearson 0.334, Spearman 0.472

Image links really start to pay off once you already have some authority.

• From mid decile tiers up, the relationship turns positive, then strengthens, and is strongest in the top deciles.
• In low-Ascore deciles (deciles 1 and 2), the images → mentions tie is weak or negative.

If you are targeting mention growth on Perplexity or Search-GPT, image links are especially productive.

• Images correlate with mentions most on Perplexity and Search-GPT (Spearman ≈ 0.55 and 0.53), then ChatGPT/Gemini (≈ 0.49 – 0.52), then Google-AI (≈ 0.46).

Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/how-ai-really-weighs-your-links-analysis-of-35000-datapoints/557253/

As a marketing specialist who has gone through the transition from Universal Analytics to Google Analytics 4 on countless projects, I can confidently say that no platform migration has divided the marketing community quite like GA4.

Five years after the initial launch of GA4 in October 2020, and more than a year since the complete Universal Analytics shutdown, it’s time for an honest review of where we stand with Google’s flagship analytics platform.

The Great Migration: A Bumpy Road To The Future

When Google announced in March 2022 that Universal Analytics would stop processing data by July 2023, the marketing world was in shock. The short window between the announcement and the sunset date caught many marketers off guard, causing mild panic among companies and website owners.

What followed was one of the most contentious platform migrations in digital marketing history.

Starting July 1, 2023, standard Universal Analytics properties stopped processing hits, with Universal Analytics 360 properties receiving a one-time processing extension ending on July 1, 2024.

For many of us who had spent over a decade mastering Universal Analytics, this wasn’t just a platform change; it was the end of an era.

The fundamental shift from UA’s session-based model to GA4’s event-based architecture represented more than a technical upgrade. It was a complete reimagining of how we measure and understand user behavior.

While Google positioned this as future-proofing for a privacy-first, cross-device world, the reality on the ground was far more challenging.

The Promise Vs. The Reality

Google’s marketing pitch for GA4 was compelling: enhanced user journey tracking, privacy-compliant measurement, advanced machine learning, and more intuitive reporting.

As someone who eagerly adopted GA4 early, I was excited about these possibilities. However, the execution has been a mixed bag at best.

The User Experience Crisis

Perhaps the most important criticism of GA4 has been its user interface, with widespread negative feedback from the marketing community.

The interface complaints aren’t just about aesthetics; they’re about productivity. Tasks that took two clicks in Universal Analytics now require six or more steps in GA4. Filtering for a single page, something marketers do dozens of times daily, has become an exercise in frustration.

Data Reliability Concerns

Beyond usability issues, GA4 has struggled with data reliability problems that strike at the heart of marketing decision-making.

According to Piwik PRO’s analysis, conversion tracking discrepancies, inaccurate traffic reports, integration problems with Google Ads, and discrepancies between GA4 data and BigQuery exports have been persistent issues since launch.

These aren’t minor technical glitches; they’re fundamental problems that affect how we measure campaign performance and allocate marketing budgets.

The shift from UA’s goal-based conversion tracking to GA4’s event-based system has created confusion around what we’re actually measuring, particularly when comparing year-over-year performance.

Signs Of Progress: Recent Improvements

To Google’s credit, it hasn’t ignored the criticism. The past year has seen several meaningful updates that address some of the most pressing concerns.

Google Analytics has introduced a Generated Insights feature that summarizes trends and changes in data, helping users make quicker decisions. These insights are displayed at the top of detail reports and include action buttons for report modifications. This AI-powered analysis is genuinely helpful for identifying patterns that might otherwise be missed.

The addition of Anomaly Detection in detail reports automatically flags any unexpected spikes or dips in your data, represented as circles on your charts. For busy marketers juggling multiple campaigns, this proactive approach to data monitoring is a welcome improvement.

Perhaps most significantly for agencies and enterprises, as of March 2025, GA4 finally supports the ability to copy reports and explorations from one property to another. If you’ve ever had to manually rebuild the same custom reports across multiple client accounts, you’ll appreciate how much time this saves.

The Broader Impact On Marketing Analytics

The GA4 transition has forced the entire marketing analytics landscape to evolve. Current data shows that over 15 million websites use GA4, making it the de facto standard for web analytics regardless of individual opinions about the platform.

Screenshot from trends.builtwith.com, August 2025

Looking into historical Universal adoption, more than 21 million websites used Universal Analytics, which leaves a gap to be filled. So, despite GA4 leading the analytics industry, it still has a long way to reach the former adoption rate, which creates some sort of vacuum.

This shift has had several unintended consequences. Many organizations have diversified their analytics stack, supplementing GA4 with specialized tools that fill specific gaps. There is an increased interest in alternatives like Matomo for privacy-focused measurement and more sophisticated attribution modeling platforms for enterprise users.

The emphasis on first-party data collection has also intensified. With the end of third-party cookies and stricter consent rules, website data coverage will decrease, limiting your leverage.

First-party data will become even more important than ever. This has pushed marketing teams to become more strategic about data collection and customer relationship building.

Practical Recommendations For Marketing Teams

After five years of working with GA4, here’s my advice for marketing teams struggling with the transition:

Invest In Education

The learning curve has been steep, but unavoidable.

As former Google Analytics team member Krista Seiden wisely noted:

“The only way to learn a new tool is to dive in and actually get your feet wet.” Budget time and resources for proper training.

Focus On Trends, Not Absolutes

When comparing year-over-year performance, focus on trends and seasonality rather than absolute numbers. GA4’s different measurement methodology means exact numerical comparisons with UA data are largely meaningless.

Supplement Strategically

Don’t try to make GA4 do everything. Identify specific gaps in your analytics needs and fill them with specialized tools.

Many successful marketing teams now use GA4 as their foundation while leveraging additional platforms for detailed attribution, customer journey mapping, or real-time optimization.

Embrace The Event-Based Model

Rather than fighting GA4’s event-based structure, lean into it. Google recommends implementing new logic that makes sense in the event-based context rather than simply copying over existing event logic from UA. This approach will yield better insights in the long run.

Looking Forward

Cookie deprecation and enhanced privacy regulations mean that features like enhanced conversions, consent mode V2, and offline conversion tracking are now necessary rather than nice-to-haves. GA4, despite its flaws, is better positioned for this privacy-first future than Universal Analytics ever was.

The platform will undoubtedly continue improving. Google has shown responsiveness to user feedback, and the recent updates demonstrate a commitment to addressing the most pressing usability concerns. However, marketers should expect GA4 to remain more complex and technical than its predecessor.

The Bottom Line

Five years after its launch, GA4 represents both the promise and peril of modern marketing analytics. It offers capabilities that Universal Analytics couldn’t match: cross-platform tracking, privacy compliance, and AI-powered insights. Yet, it also demands a level of technical sophistication that many marketing teams struggle to achieve.

The forced migration was undoubtedly painful, and the criticism of GA4’s usability is largely justified. However, the platform is here to stay, and fighting that reality serves no one. The organizations that will thrive are those that invest in proper GA4 implementation, supplement it strategically with other tools, and adapt their processes to work with rather than against its event-based philosophy.

As marketers, we’ve weathered platform changes before, and we’ll weather this one, too. The key is approaching GA4 not as a replacement for Universal Analytics, but as a fundamentally different tool for a fundamentally different digital landscape. Once we make that mental shift, GA4 becomes less frustrating and more powerful.

The future of marketing analytics is privacy-first, cross-platform, and AI-enhanced. GA4, for all its current limitations, is our best free gateway to that future. It’s time to stop mourning Universal Analytics and start mastering what comes next.

More Resources:

Featured Image: kenchiro168/Shutterstock

https://www.searchenginejournal.com/ga4-years-later-the-current-state-of-marketing-analytics/553179/

New research from BrightEdge offers insights into how Google’s AI Overviews ranks websites across different verticals, with implications for what SEOs and publishers should be focusing on.

AIO And Organic Search

The data shows that 54% of the AI Overviews citations matched the web pages ranked in the organic search results. This means that 46% of citations do not overlap with organic search results.  Could this be an artifact of Google’s FastSearch algorithm?

Google’s FastSearch is based on ranking signals generated by the RankEmbed deep-learning model that is trained on search logs and third-party quality raters. The search logs consist of user behavior data, what Google terms “click and query data.” Click data teaches the RankEmbed model about what users mean when they search.

Click behavior is feedback about queries and relevant documents, similar to how the ratings submitted by the quality raters teach RankEmbed about quality. User clicks are a behavioral signal of which documents are relevant. So, as a hypothetical example, if people who search for “How to” tend to click on videos and tutorials, this teaches the model that videos and tutorials tend to satisfy those kinds of queries. RankEmbed “learns” that documents that are semantically similar to a tutorial are good matches for that kind of query. The models aren’t learning in a human sense; they are identifying patterns in the click data.

This doesn’t mean that the 54% of AIO-ranked sites are there because of traditional ranking factors. It could be that the FastSearch algorithm retrieves results that are similar to the regular search results 54% of the time.

Insight About Ranking Factors

BrightEdge’s data could be reflecting the complexity of Google’s FastSearch algorithm, which prioritizes speed and semantic matching of queries to documents without the use of traditional ranking signals like links. This is something that SEOs and publishers should stop and consider because it highlights the importance of content and also the importance of matching the type of content that users prefer to see.

So, if they’re querying about a product, they don’t expect to see a page with an essay about the product; they expect to see a page with the product.

Organic And AIO Overlap Evolved Over Time

When AIO launched, there was only about a 32% overlap between AIO and the classic organic search results. BrightEdge’s data shows that the overlap has grown over the sixteen months between the debut of AI Overviews and today.

Organic And AIO Match Depends On The Vertical

The 54/46 percentage split isn’t across the board. The percentage of AIO-ranked sites that match the organic search results varies according to the vertical.

Your Money Or Your Life (YMYL) content showed a higher rate of overlap between organic and AIO.

BrightEdge’s data shows:

• Healthcare has a strong overlap: 75.3% overlap (began at 63.3%).
• Education overlap has increased significantly: 72.6% overlap between organic and AIO, showing +53.2 percentage points growth, from 19.4% to 72.6%.
• Insurance also experienced increased overlap: 68.6%. That’s a +47.7 percentage points growth from the 20.9% overlap when AIO was first introduced.
• E-commerce has very little overlap with the organic search results: 22.9% overlap (only +0.6 percentage points change).

I’m going to speculate here and say that Healthcare, Education, and Insurance search results may have a strong overlap because the pool of authoritative sites that users expect to see may be smaller. This may mean that websites in these verticals may have to work hard to be the kind of site that users expect to see. A broad and simplified explanation is that FastSearch does not use traditional organic search ranking factors. It’s ranking the kinds of web pages that match user expectations, meet certain quality standards, and are semantically relevant to the query.

Related: Google AI Overviews Impact On Publishers & How To Adapt Into 2026

What Is Going On With E-Commerce?

E-commerce is the one area where overlap between organic and AIO remained relatively steady with very little change. BrightEdge notes that AIO coverage actually decreased by 7.6%. AIO may be a good fit for research but is not a good format for users who are ready to make a purchase.

Final Takeaways

Although BrightEdge recommends focusing on traditional SEO for sites in verticals that have over 60% of overlap with organic search, it’s a good idea for all sites, regardless of vertical, to focus on traditional SEO and also to focus on precision, matching user expectations for each query, and pay attention to what users are saying so as to be able to react swiftly to changing trends.

BrightEdge offers the following advice:

“Step 1: Identify Your Overlap Profile Measure what percentage of your AI Overview citations also rank organically and benchmark against the 54% average to understand where you stand.

Step 2: Match Strategy to Intent. High overlap (>60%) means focus on SEO; low overlap (<30%) requires split content strategies; growing overlap (30-60%) needs comprehensive content serving both.

Step 3: Monitor the Convergence Track your overlap percentage monthly as it has grown +22% industry-wide in 16 months, watching for shifts like September 2024’s +5.4% jump.”

Read BrightEdge’s report:

AI Overview Citations Now 54% from Organic Rankings

https://www.searchenginejournal.com/google-ai-overviews-overlaps-organic-search-by-54/557317/

OpenAI launched the Sora iOS app, beginning an invite-based rollout in the United States and Canada.

With Sora, OpenAI appears to be releasing its first non-ChatGPT consumer app and its first social product.

The app runs on the newly released Sora 2 model for video and synchronized audio.

What’s The Sora App?

Sora is positioned as a creation-first social experience rather than a public-broadcast platform.

It adds social features on top of Sora 2’s generation capabilities, including tools to remix videos and collaborate with friends inside the app.

Custom Feed

The app uses OpenAI’s language models to power a recommender algorithm that accepts natural language instructions.

Users can customize their feed through conversational commands rather than buried settings menus.

By default, the feed prioritizes content from people users follow or interact with.

The Sora team wrote:

“We are not optimizing for time spent in feed, and we explicitly designed the app to maximize creation, not consumption.”

Cameos

Sora centers on “cameos,” which let you place yourself or friends inside AI-generated scenes after a short one-time video and audio capture in the app.

OpenAI says people who appear in cameos control who can use their likeness and can revoke access or remove any video that includes it.

Content Creation

Beyond cameos and feed browsing, the app lets users create original videos through text prompts and remix other users’ generations.

The underlying Sora 2 model can follow multi-shot instructions, maintain world state across scenes, and generate synchronized dialogue and sound effects.

ChatGPT Pro subscribers can access an experimental higher-quality Sora 2 Pro model on sora.com, with app access planned.

The original Sora 1 Turbo remains available, and existing user content stays in personal libraries.

Monetization

OpenAI plans to keep Sora free initially, with generation limits determined by available compute resources.

The company’s revenue strategy involves charging users for extra generations when demand surpasses capacity. No plans for advertising or creator revenue sharing have been announced.

Availability

The app operates on an invite-only basis, with sign-ups available through the iOS app. The App Store listing is live.

Image Credit: Apple App Store

OpenAI says it made Sora invite-only to ensure users arrive with friends already in the app. The company cites feedback indicating that cameos drive the experience, making existing connections essential.

Looking Ahead

For marketers and creators, Sora serves as a new platform for distributing short, AI-generated videos, affirming OpenAI’s focus on developing consumer-oriented tools.

Sora’s adoption will largely depend on accessibility, real-world applications, and how well the feed encourages active creation instead of passive viewing.

Featured Image: Robert Way/Shutterstock

https://www.searchenginejournal.com/openai-launches-sora-ios-app-alongside-sora-2-video-model/557302/