OpenAI has launched a new app ecosystem within ChatGPT, along with a preview of the Apps SDK, enabling developers to create conversational, interactive applications based on the Model Context Protocol.
These apps are now accessible to all logged-in ChatGPT users outside the European Union, across Free, Go, Plus, and Pro plans.
Early partners include Booking.com, Canva, Coursera, Expedia, Figma, Spotify, and Zillow.
How ChatGPT Apps Work
Apps naturally integrate into conversation, and you can activate them by name, such as saying, “Spotify, make a playlist for my party this Friday.’
When using an app for the first time, ChatGPT prompts you to connect and clarifies what data might be shared. For example, OpenAI demonstrates ChatGPT suggesting the Zillow app during a home-buying discussion, allowing you to browse listings on an interactive map without leaving the chat.
John Weisberg, Head of AI at Zillow, said:
“The Zillow app in ChatGPT shows the power of AI to make real estate feel more human. Together with OpenAI, we’re bringing a first-of-its-kind experience to millions — a conversational guide that makes finding a home faster, easier, and more intuitive.”
Developer Opportunities & Reach
OpenAI positions the Apps SDK as a way to “reach over 800 million ChatGPT users at just the right time.”
The SDK is open source and built on MCP, allowing developers to create their own chat logic and custom interfaces. You can also connect to your own backends for login and premium features, and easily test everything through Developer Mode in ChatGPT.
OpenAI has provided detailed documentation, design guidelines, and example apps to support developers.
Submission & Monetization
Developers can begin building immediately. OpenAI has announced that formal app submissions, reviews, and publication will commence later this year, along with a directory for browsing and searching apps.
Additionally, the company plans to disclose monetization details, including support for the Agentic Commerce Protocol, which enables instant checkout within ChatGPT.
Safety & Privacy
All apps must follow OpenAI’s policies, be audience-appropriate, and have clear third-party rules. Developers should provide privacy policies, collect only necessary data, and be transparent about permissions.
OpenAI’s draft guidelines also require apps to be purposeful, avoid misleading designs, and manage errors effectively. Submissions must demonstrate stability, responsiveness, and low latency; apps that crash or hang will be rejected.
Rollout & Availability
Today’s rollout does not include EU users, but OpenAI has announced plans to introduce these apps to that region soon.
Additionally, eleven more partner apps are scheduled for release later this year. OpenAI also intends to expand app availability to ChatGPT Business, Enterprise, and Education plans.
Looking Ahead
Apps that appear within AI-led conversations could transform the way services are found and accessed.
Instead of relying on traditional rankings or app-store positions, visibility might be driven more by conversational relevance and demonstrated value within the chat.
Teams responsible for app functionality should think about how users will naturally request these services and identify the key moments when ChatGPT is likely to recommend them.
Creators have reported view declines on YouTube since mid-August.
YouTube’s official Liaison account says the change wasn’t on YouTube’s side and points to a widely shared explanation on X: an ad-blocking list update that interferes with how views are logged.
What YouTube Has Said
Responding to creators, YouTube’s Creator Liaison wrote:
“The change wasn’t on YouTube’s side… this is the most common explanation I’ve personally seen.”
The post links to an analysis that attributes the decline to the EasyPrivacy update. YouTube hasn’t announced any separate product or policy changes related to view counting.
The change wasn’t on YouTube’s side. A bunch of non-YouTube people have looked into it and this is the most common explanation I’ve personally seen: https://t.co/Da7C4z6Xz5
Tech creator ThioJoe highlighted an EasyPrivacy update that added a rule blocking the request youtube.com/api/stats/atr.
The thread argues that blocking this request may prevent the player from sending the data YouTube uses to log a view.
TL;DR: The YouTube view drops are because of the EasyPrivacy list adding the URL “youtube[.]com/api/stats/atr” on August 11th. Causing Adblockers to block view counting telemetry.
The blocked merged into UbO Lite on August 12th, pushed to chrome webstore by next day.
The EasyPrivacy commit shows the single-line addition in easyprivacy_specific.txt for ||youtube.com/api/stats/atr.
EasyPrivacy is a community-maintained filter list developed by the EasyList project. Ad blockers use these lists to determine which network requests to block. EasyPrivacy specifically targets tracking requests like analytics and behavioral beacons to help reduce data collection.
Several popular ad blockers, such as uBlock Origin Lite, include EasyPrivacy as part of their default or recommended list of filters.
Why This Affects YouTube Views
When a YouTube video plays, the player sends small background requests to log what happened. Think of them as receipts that say a playback started or progressed.
The thread at the center of this discussion points to one of those requests, …/api/stats/atr, as being blocked by the EasyPrivacy change.
If that request is blocked, a playback may not be recorded as a view in analytics even though the video still loaded for the viewer.
What Creators Reported
Posts discussing the issue indicate that the timing of the drops coincides with the EasyPrivacy change, and the rule was incorporated into uBlock Origin Lite shortly afterward, according to posts in the same thread.
These posts also note that the impact is most noticeable on desktop, where browser extensions are more common, while mobile app viewing seems less affected. Some creators have mentioned that their RPM remained relatively stable despite a decrease in raw views, and their like-to-view ratios increased because likes still count even when some views do not.
These insights come from public threads and are not official platform-wide metrics from YouTube.
Why This Matters
If you noticed unexplained drops starting mid-August, some of the decline might be due to ad-blocked sessions rather than a change in audience interest or YouTube policies.
For reporting and planning, compare trends on desktop and mobile, review revenue and watch-time alongside view counts, and note the affected period for teams and clients.
The key takeaway is that updates to third-party filter lists can influence your analytics data even if the platform itself remains unchanged.
Builderius WordPress website builder announced the ability to develop sites using GraphQL together with AI. The new functionality enables developers to use the power of GraphQL with the assistance of AI.
Why GraphQL
GraphQL can be a more efficient way to fetch data than traditional approaches in WordPress, using visual query builders, PHP, or REST API. It enables websites, or in this case Builderius, a visual builder for WordPress, to fetch only the specific data they need in one request, reducing the inefficiencies of how dynamic data is typically fetched within WordPress. Unlike the WordPress REST API, which delivers fixed sets of data from multiple endpoints, GraphQL gives developers more control and efficiency by returning exactly what’s asked for in a single query.
AI-Assisted Learning Setup
Builderius provides schema documentation and setup instructions that enable AI tools (like Claude or ChatGPT) to function as teaching assistants for learning GraphQL. Users are able to learn GraphQL through step-by-step project work as the AI guides them, explaining, structuring, and improving queries. This approach helps users learn GraphQL concepts while applying them in actual WordPress projects, supporting both productivity and ongoing learning.
Builderius is providing schema documentation and configuration guides that enable AI tools to act as interactive learning partners for getting started using GraphQL within Builderius. Instead of merely generating code, the AI integration helps users understand the structure and reasoning behind queries, enabling them to apply GraphQL concepts effectively in real development work. This approach blends hands-on learning with practical application, helping developers become proficient while building dynamic WordPress sites.
According to Builderius:
“Once configured, you can simply start new conversations by asking what you want to build. The AI will remember its role and your learning progression across all chats in the project.
…Your AI will explain the relationship between built-in WordPress queries, custom GraphQL queries, and dynamic data tags. You’ll understand how data flows from your queries into your visual layouts, with concrete examples.”
AI Mode is Google’s most powerful AI search experience, providing answers to complex questions in a way that anticipates the user’s information needs. Although Google says that nothing special needs to be done to rank in AI Mode, the reality is that SEO only makes pages eligible to appear.
The following facts, insights, and examples demystify AI Mode and offer a clear perspective on how pages are ranked and why.
What Is AI Mode?
Google’s AI Mode was introduced on March 5, 2025, as an experiment in Google Labs, then swiftly rolled out as a live Google search surface on May 20. AI Mode is described as its most cutting-edge search experience, combining advanced reasoning with multimodality. Multimodality means content beyond text data, such as images and video content.
AI Mode is a significant evolution of Google Search that encourages users to research topics. This presents benefits and changes to how search works:
The benefit is that Google is citing a greater variety of websites per query.
The change is that websites are being cited for multiple queries, beginning with the initial query plus follow-up queries.
Those two factors present challenges to SEO. For example, do you optimize for the initial query, or what can be considered a more granular follow-up query? Most SEOs may consider optimizing for both.
Query Fan-Out
Similar to AI Overviews, AI Mode uses what they call a query fan-out technique, which divides the initial search query into subtopics that anticipate further information the user may need.
Query fan-out anticipates the user’s information journey. So, if they ask question A, Google’s AI Mode will show answers to follow-up questions about B, C, and D.
For example, if you ask, “What is a mechanical keyboard?” Google answers the following questions:
What is a mechanical keyboard?
What are mechanical switches?
What happens when a key is pressed on a mechanical keyboard?
What are keycaps and what materials are they made from?
What is the role of the printed circuit board (PCB)?
How are mechanical switches categorized?
The following screenshot of the AI Mode search result shows the questions (in red) positioned next to the answers, illustrating how query fan-out generates related questions and creates answers for them.
Screenshot of query fan-out in AI Mode, September 2025
How I Extracted Latent Questions From AI Mode Search Results
The way I extracted the questions that query fan-out is answering was by doing an inverse knowledge search, also known as reverse QA.
I copied the output from AI Mode into a document, then uploaded it to ChatGPT with the following prompt:
Read 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 that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference.
Try that with AI Mode to get a better understanding of the underlying questions it generates with query fan-out. This will help clarify what is happening and make it less mysterious.
Content With Depth
Google’s advice to publishers who want to rank in AI Mode is to encourage them to create content that engages users who are conducting in-depth queries:
“…users are asking longer and more specific questions – as well as follow-up questions to dig even deeper.”
That may not mean creating giant articles with depth. It just means focusing on the content that users are looking for. That approach to content is subtly different from chasing keyword inventory.
Google recommends:
Focus on unique, valuable content for people.
Provide a great page experience.
Ensure we can access your content.
Manage visibility with preview controls. (Make use of nosnippet, data-nosnippet, max-snippet, or noindex to set your display preferences.)
The last two recommendations require further clarification:
Understand The Full Value Of Your Visits
This is an encouragement to focus on delivering the information needs of the user and to note that focusing too hard on the “click” comes at the expense of providing what an “engaged” audience is looking for.
Evolve With Your Users
Google frames this as evolving along with how users are searching. A more pragmatic view is to evolve with how Google is showing results to users.
What Experts Say About Content Structure For AI Mode
Duane Forrester, formerly of Bing Search, advises that content needs to be structured differently for AI search.
“…the search pipeline has changed. You don’t need to rank – you need to be retrieved, fused, and reasoned over by GenAI systems.”
In his article titled “Search Without A Webpage,” he expands on the idea that content must be useful as forming the basis of an answer:
“…your content doesn’t have to rank. It has to be retrieved, understood, and assembled into an answer.”
He also says that content needs to be:
“…structured, interpretable, and available when it’s time to answer.
This is the new search stack. Not built on links, pages, or rankings – but on vectors, embeddings, ranking fusion, and LLMs that reason instead of rank.”
When Duane says that content needs to be structured, he’s referring to on-page structure that communicates not just the hierarchy of information but also offers a clean delineation of what each section of content is about.
In my opinion:
Paragraphs should consist of sentences that build to an idea, with a clear payoff at the end.
If a sentence doesn’t have a purpose within the paragraph, it’s probably better to remove it.
If a paragraph doesn’t have a clear purpose, get rid of it.
If a group of paragraphs is out of place near the end of the document, move it closer to the beginning if that’s where it belongs.
The entire document should have a clear beginning, middle, and end, with each section serving as “the basis of an answer.”
“Use clear, specific language: LLMs rely on clarity first and foremost, so avoid using too many pronouns or any other vague, undefined references.
Organize your content predictably: Break your content up into sections and use headings, like H2 and H3, to organize the unique ideas central to your article’s thesis.”
Mordy Oberstein, founder of Unify Marketing, explains that the focus on attribution took precedence for the average digital marketer:
“What resonates with the person hasn’t fundamentally changed, and I don’t think we’ve realized that. I think we’ve forgotten. I think we’ve completely forgotten what resonance is as digital marketers because of the advent of two things with the internet:
Attribution
The ability to track responses
Businesses were seemingly OK with digital marketers doing whatever it took to get that traffic, to get that conversion, because that’s just the Internet, so everyone just goes along.
Now, with AI Mode, attribution no longer exists in the same way.”
Mordy’s right about attribution. AI Mode cannot be tracked in Google Analytics 4 or Google Search Console. They’re lumped into the Web Search bucket, so there’s no way to tell where it’s coming from. It can’t be distinguished from regular organic search in either GA4 or GSC.
“But the organic side, there is an area … that is zero click. So zero click is for those audience members who don’t know what that means, zero click means when you are having a conversation with AI, for example, I’m trying to compare two different running shoes and I’m having this, ‘what’s going to be better for me?’
I’m having a conversation with AI and AI is pooling and referencing … whatever winning schema formats and content that are out there … but it’s zero click. It’s not going to your site. It’s not going there. So without this data that really affects … organic content strategy.”
And that dovetails with what Mordy is getting at, that SEOs are conditioned to view internet marketing through the “attribution” lens, but that we may be entering a kind of post-attribution period, which is what it largely was pre-internet. So, the old marketing strategies are back in, but they were always good strategies (building awareness and popularity); it’s just that digital marketers tended to engage more with attribution.
Mordy shares the example of someone researching a brand of sneakers, who asks a chatbot about it, then goes to Amazon to see what it looks like and what people are saying about it, then watches video reviews on YouTube, and then goes to AI Mode to review the specs. After all that research, the consumer might return to Amazon and then head over to Google Shopping to compare prices.
He concludes with the insight that resonating with users has always been important, and that very little has changed in terms of consumers conducting research prior to making a purchase:
“That was all happening before. But now the perception is that it’s happening because of LLMs. I don’t think things have fundamentally changed.”
I think that the key insight here is that the research is still happening exactly as before, but what’s changed is that the opportunities to expose your business or products have expanded to multimodal search surfaces, especially with AI Mode.
The screenshot below shows how Nike is taking charge of the conversation on AI Mode with both text and video content.
Screenshot of citations and videos in AI Mode, September 2025
Connect Your Brand To A Product
It’s becoming evident that connecting a brand semantically to a service or product may be important for communicating that the brand is relevant to whatever you want it to be relevant for.
Below is a screenshot of a sponsored post that’s indexed by Google and is ranking in AI Mode for the keyword phrase “what are ad hijacking tools.”
Screenshot of sponsored post ranking in AI Mode, September 2025
SEO Makes Content Eligible For AI Mode
SEO best practices are necessary to be eligible to appear in AI Mode. That’s different from saying that standard SEO will help you rank in AI Mode.
This is what Google says:
“To be eligible to be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to be shown in Google Search with a snippet, fulfilling the Search technical requirements. There are no additional technical requirements.”
The page works, meaning that Google receives an HTTP 200 (success) status code.
The page has indexable content.”
Google clearly says that foundational SEO is necessary to be eligible to rank in AI Mode. But it does not explicitly confirm that SEO will help a site rank in AI Mode.
Is SEO Enough For AI Mode?
Google and Googlers have reassured publishers and SEOs that nothing extra needs to be done to rank in AI search surfaces. They affirm that standard SEO practices are enough.
Standard SEO practices ensure that a site is crawled, indexed, and eligible for ranking in AI Mode. But there is implication that the signals for actually ranking in AI Mode are substantially different from standard organic search.
What Is FastSearch?
Information contained in recent Google antitrust court documents shows that AI Mode ranks pages with a technology called FastSearch.
FastSearch grounds Google’s AI search results in facts, including data from the web. This is significant because FastSearch uses different ranking signals from what’s used in the regular organic search, prioritizing speed and selecting only a top few pages for AI grounding.
The recent Google antitrust trial document from early September offers this explanation of FastSearch:
“To ground its Gemini models, Google uses a proprietary technology called FastSearch. … FastSearch is based on RankEmbed signals—a set of search ranking signals—and generates abbreviated, ranked web results that a model can use to produce a grounded response. …
FastSearch delivers results more quickly than Search because it retrieves fewer documents, but the resulting quality is lower than Search’s fully ranked web results. “
And elsewhere in the same document:
“FastSearch is a technology that rapidly generates limited organic search results for certain use cases, such as grounding of LLMs, and is derived primarily from the RankEmbed model.”
RankEmbed
RankEmbed is a deep learning model that identifies patterns in datasets and develops signals that are used for ranking purposes. It uses a combination of user data from search logs and scores generated by human raters to create the ranking-related signals.
“RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: __% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.
The RankEmbed model itself is an AI-based, deep learning system that has strong natural-language understanding. This allows the model to more efficiently identify the best documents to retrieve, even if a query lacks certain terms.”
Human-Rated Data
The human-rated data, which is part of RankEmbed, is not used to rank webpages. Human-rated data is used to train deep learning models so they can recognize patterns that correlate with high and low-quality webpages.
How human-rated data is used in general:
Human-rated data is used to create what are called labeled data.
Labeled data are examples that models use to identify patterns in vast amounts of data.
In this specific instance, the human-labeled data are examples of relevance and quality. The RankEmbed deep learning model uses those examples to learn how to identify patterns that correlate with relevance and page quality.
Search Logs And User Behavior Signals
Let’s go back to how Google uses “70 days of search logs” as part of the RankEmbed deep learning model, which underpins FastSearch.
Search logs refer to user behavior at the point when they’re searching. The data is rich with a wide range of information, such as what users mean when they search, and it can also include the domain names of businesses they associate with certain keywords.
The court documentation doesn’t say all the ways this data can be used. However, a Google antitrust document from May 2025 revealed that search log (click) patterns only become meaningful when scaled to the billions.
Some SEOs have theorized that click data can directly influence the rankings, describing a granular use of clicks for ranking. But that may not be how click data is used, because it’s too noisy and imprecise.
“Some Known Shortcomings of Live Traffic Eval The association between observed user behavior and search result quality is tenuous. We need lots of traffic to draw conclusions, and individual examples are difficult to interpret.”
It’s fair to say that search logs are not used to directly impact the rankings of an individual webpage, but are used to learn about relevance and quality from user behavior.
FastSearch is not the same ranking algorithm as the one used for organic search results. It is based on RankEmbed, and the term “embed” suggests that embeddings are involved. Embeddings map words into a vector space so that the meaning of the text is captured. For SEO, this means that keyword relevance matters less, and topical relevance and semantic meaning carry more weight.
Google’s statement that standard SEO is all that’s needed to rank in AI Mode is true only to the extent that standard SEO will ensure that the webpage is crawled, indexed, and eligible for the final stage of AI Mode ranking, which is FastSearch.
But FastSearch uses an entirely different set of considerations at the LLM level to decide what will be used to answer the question.
In my opinion, it’s more realistic to say that SEO best practices make webpages eligible to appear in AI Mode, but the ranking processes are different, and so new considerations come into play.
SEO is still important, but it may be useful to focus on semantic and topical relevance.
AI Mode Is Multimodal
AI Mode is multimodal, meaning image and video content rank in AI Mode. That’s something that SEOs and publishers need to consider in terms of how user expectations drive content discovery. This means it may be useful to create image, video, and maybe even audio content in addition to text.
Optimizing Images For AI Mode
Something that is under your control is the featured image and the in-content images that go with your content. The best images, in my opinion, are images that are noticeable when displayed in AI Mode and contain visual information that is relevant to the search query.
Here’s a screenshot of images that accompany the cited webpages for the query, “What is a mechanical keyboard?”
Screenshot from AI Mode, September 2025
As you can see, none of the images pop out or call attention to themselves. I don’t think that’s Google’s preference; that’s just what publishers use. Images should not be an afterthought. Make them an integrated part of your ranking strategy for AI Mode.
Creative use of images, in my opinion, can help a page call attention to itself as useful and relevant. The best images are ones that look good when Google crops them into a square format.
Google AI Mode is multimodal, which means optimizing your images so that they display well in AI Mode search results. Your images should be attractive regardless of whether they are displayed as either a rectangle (approximately 16:9 aspect ratio) or a square (approximately 4:3 aspect ratio).
Mordy Oberstein offers these insights on multimodal marketing:
“AI Mode is looking at videos, images, and yes, you could do all of that. Yes, you should do all of that – whatever is possible to do while being efficient and not getting misdirected or losing focus – yes, go ahead. I’m all for creating authoritativeness through content. I think that’s an essential strategy for pretty much any business.
AI Mode is not just looking at your website content, whether it’s your image content, audio content, whatever it may be, it’s also looking at how the web is talking about you.”
AI Mode Is Evolution, Not Extension
AI Mode is not just an extension of traditional search but an evolution of it. Search now includes text, images, and video. It anticipates follow-up queries and displays the answers to them using the query fan-out technique. This shifts the SEO focus away from keyword inventory and chasing clicks and toward considering how the entire user information journey is best addressed and then crafting content that satisfies that need.
Where is search going to develop? Is ChatGPT a threat or an opportunity? Is optimizing for large language models (LLMs) the same as optimizing for search engines? These are some of the critical questions that are top of mind for both SEOs and CMOs as we head into a multi-search world.
In Part 2 of this two-part interview series, I try to answer these questions based on data from our internal research to provide some clear direction and focus to help navigate considerable change. If you haven’t already, go back and read Part 1.
What you will learn in this Part 2:
Traditional Search Engine Results Page (SERP) Evolution: Why traditional search isn’t dying but fundamentally transforming, where it still excels, and how it is part of Google’s integrated approach to AI evolution.
Google AI Mode Strategy: How AI Mode and AI Overview operate as the same strategy at different thresholds, with AI Mode being 2.1x more likely to include brands while AI Overview remains highly selective.
Agentic AI Revolution: Why 33% of organic searches now come from AI agents browsing on behalf of users, creating real-time interactions that demand immediate content accessibility.
Search Funnel Transformation: How the customer journey has evolved from linear progression to unpredictable funnel-stage jumping, with AI handling research while conversion still happens through traditional organic channels.
The Three Pillars Framework: Why CMOs need reporting for early AI shift detection, automation for seamless AI-readiness, and strategic recommendations to influence how AI tells their brand’s story.
Do You Think There Is Any Future For Traditional SERP Search, Or Do You Think It Will Become Obsolete?
I think we’re witnessing more of an evolution than an extinction. Traditional SERP search has a future, but it’s going to look completely different.
According to our internal data, 92% of all searches happen here. And when it comes to meaningful actions, such as downloads, sign-ups, or purchases, 95% start on Google. Search volume hasn’t gone down – it’s actually grown 10% year-over-year. With AI Mode, Google is layering AI directly into the experience.
The takeaway is clear: AI hasn’t replaced traditional SERPs; it’s utilizing and aligning with them.
Image from author, September 2025
Where Traditional Search Still Excels
Traditional search still absolutely shines in certain areas. When you’re dealing with complex queries or personal searches, those traditional SERPs still provide something AI cannot: depth, discernment, and diverse perspectives. Ecommerce is a perfect example – when shopping, I still want to see those traditional listings to compare sources, read different reviews, and check various offers.
Traditional SERP’s And Google’s Integrated Approach
Google is handling this integration cleverly. They’re not replacing classic SERPs; they’re augmenting them. Google’s Gemini model powers AI Overviews that appear above traditional listings, creating comprehensive summaries from multiple sources. Classic SERPs provide the foundational data, and AI distills and presents it in new, user-centric ways.
For brands and CMOs, this creates a new optimization challenge. You’re not just thinking about traditional SEO anymore; you need to optimize for AI inclusion, too. If you get cited in an AI summary, your visibility increases dramatically. It’s an interesting paradox where fewer traditional listings appear, but cited sources gain more prominence.
We’re seeing conversational capabilities, multimodal search with images and video, and direct answers that go way beyond static blue links. Users can now ask follow-up questions, search with photos, or engage in natural language conversations – capabilities that would have been impossible with traditional link-based results.
When AI Search Meets Traditional SEO
The overlap between AI citations and traditional search results has grown 22.3% since 2024. However, this varies significantly by industry, making your vertical a key factor in strategy development.
The variation is substantial. Ecommerce saw minimal change at 0.6 percentage points, while Education increased by 53.2 percentage points. Your industry determines the approach you should take.
In YMYL sectors like Healthcare, Insurance, and Education, overlap reaches 68-75%. When trust is critical, Google tends to favor content that already performs well in traditional search rankings.
Ecommerce operates differently. Overlap remained flat, and AI Overview coverage actually decreased by 7.6 percentage points. Google appears to maintain separation between shopping queries and AI answers, likely to preserve the transactional flow that drives commerce.
Image from BrightEdge, September 2025
The Interconnected Search And AI Engine Ecosystem
What’s happening is that AI Overviews are acting as content curators, selecting which sources to reference and cite. This means your content needs to be clear, authoritative, and structured in ways that both humans and AI can easily understand and extract value from. The fundamentals of relevant content – quality, clarity, technical optimization – they’re more critical than ever.
The likes of ChatGPT and Perplexity tap into traditional search engines for factual grounding, so this interconnected ecosystem is becoming the norm. It’s not just about ranking on SERPs anymore; it’s about being discoverable across multiple channels: social search, AI interfaces, traditional SERPs, and whatever comes next.
The New Traditional CMO, SEO, And AI Reality
But those traditional foundations remain crucial – they just serve both humans and AI now. For straightforward, fact-based queries, AI can generate instant answers, removing the need to browse multiple results. But for anything complex, local, or transactional, those classic blue links still appear, sometimes as fallback options, or often as primary results depending on the query type.
However, it’s worth noting that AI Overview shares the screen with classic SERPs and ads. Still, your visibility may significantly increase when you get cited in an AI-generated summary, a paradox in which traditional results may decline, but referenced sources tend to become more prominent.
Keeping Pace With Change
The pace of change is also something CMOs need to prepare for. Google’s AI Mode is evolving incredibly quickly – features, user interface (UI) presentation, and citation logic change frequently. You need to invest in technology and teams that provide real-time insights into SERP and AI Mode visibility. Keep new AI entrants on your radar, and their experimentation and pilot projects, which are crucial for understanding what drives referenced visibility and conversions through AI sources.
The role of traditional SERPs is not dying. AI and traditional search work hand in hand; it’s now Google’s default approach, and both systems co-exist beautifully, serving diverse needs within the same search journey.
What Do You Think CMOs Should Consider About How Google AI Mode Might Change An Enterprise Approach?
This is one of the most significant strategic shifts CMOs are facing right now, and it’s happening fast. Google’s AI Mode is fundamentally changing how enterprise visibility, engagement, and measurement work across search and discovery channels.
Understanding Google’s AI Strategy: AI Overviews And AI Mode
Our recent analysis reveals that AI Mode and AI Overview are not distinct strategies. They’re the same strategy but operating at different thresholds.
Think of it this way: AI Mode acts as the broad discovery engine. It’s 2.1x more likely to include brands (compared to AI Overviews), surfaces more unique brands overall, and maintains pretty stable week-over-week patterns. When it shows sources, you’ll see fewer but more prominent source cards. It’s casting a wide net with lower barriers to entry.
AI Overview, on the other hand, is the dynamic curator. It’s much more selective – only including brands in 43% of responses – but shows significantly higher volatility, which tells us the algorithm is actively evolving.
AI Mode provides stable, broad discovery, whereas AI Overviews are where Google tests new ranking approaches with much higher selectivity. It’s clever – they’re serving different user needs while continuously refining their AI capabilities.
The Multi-Query Reality Of Google AI Search
An AI query is never just one search anymore. AI Mode runs dozens of queries on behalf of the user before showing an answer.
That one question – “What’s a good treadmill for beginners?” – becomes dozens of searches instantly. Google breaks it down into features, price comparisons, reviews, safety tips, compact options, and warranty information. The AI runs these searches in parallel, pulls results, and stitches them together into a single conversational answer.
It’s no longer about matching one keyword. You’re competing to be included across the entire web of related questions that the AI asks on the user’s behalf.
AI Mode And Living In The Browser
Think about how much time you spend in your browser every day. Now imagine if it could actually think alongside you. That’s exactly what’s happening with Google Chrome’s latest AI features, and honestly, it’s pretty mind-blowing.
Here’s what’s new: AI Mode lets you ask complex questions right in the address bar – no more opening countless tabs just to find answers. Planning a trip? Chrome’s multi-tab intelligence can now pull information from all your open tabs and create one coherent plan. And soon, agentic browsing will let Gemini handle the boring stuff like booking appointments while you focus on what actually matters.
The cool thing is, AI Mode isn’t replacing Google – it’s just giving us a smarter way to use it. Think conversational search, but built right into where you already spend most of your time.
For CMOs and marketing teams, this means rethinking how people will find and interact with your content. We’re not just optimizing for search anymore; we’re optimizing for conversation.
The CMO Content Strategy And Keeping Pace With Change
Your content strategy needs a complete rethink. AI Mode pulls directly from content to generate overviews and summaries, which means you can’t just optimize for traditional SEO anymore. Your content needs to serve both AI and human audiences simultaneously. The goal is not just to rank anymore; it’s also to be selected for AI-generated overviews.
CMOs need to prepare for the pace of change. Google’s AI Mode is advancing at a rapid pace, with frequent shifts in features, UI presentation, and citation logic. You need to invest in tools and teams that provide real-time insights into SERP and AI Mode visibility.
How Are Agentic AI Agents (Crawlers And Bots) Changing The Search Funnel? How Might These Changes Impact Roles On The CMO And The SEO Team?
We’re seeing a major shift in how content gets discovered and delivered, as new types of AI agents engage with websites and surface information in real-time conversations. AI agents are now browsing on behalf of users. Unlike classic crawlers, it’s not about indexing pages to be served up later; it’s real-time interactions. If you have a dead page, or it can’t interpret what your content is saying, you lose that moment.
The Rise Of AI Agent Website Interaction
They’re acting like digital assistants – researching, comparing, recommending. If your page is slow, or your content isn’t clear, they move on instantly. They are your future customers – potential new clients – arriving through AI. In the last month, we’ve seen visits from ChatGPT’s new Agent crawler double in visits to customer websites. 33% of all organic searches are from these agents. The growth is massive.
The AI Agent Preprocessing Layer
This creates a preprocessing layer that influences every subsequent customer interaction. Unlike traditional crawlers that simply index content, these systems navigate websites, submit forms, compare options, and make recommendations on behalf of the user in real-time. Each visit represents AI doing a search on your customer’s behalf, looking for content to help explain, recommend, and help your customers in a conversation.
How This Impacts The Evolution Of The Customer Journey
The awareness phase has evolved from user-driven discovery to “pre-aware” algorithmic surfacing where AI agents proactively recommend options based on context, preferences, and behavioral patterns – often before users consciously realize they need information. Modern buyer behavior no longer follows a straight-line progression. Instead, customers jump between funnel stages unpredictably, sometimes moving directly from initial awareness to making purchases, or cycling back to discovery phases for related products.
AI Search Users: Enter the funnel at the research and exploration stage, asking questions and gathering information to inform their decisions. They’re seeking understanding, not yet ready to transact.
Organic Search Users: Demonstrate clearer purchase intent, often searching for specific products, services, or solutions. They know what they want and are closer to conversion.
The Journey Dynamic: Many users begin with AI-powered research but ultimately convert through organic search or direct channels – making AI search valuable for top-of-funnel discovery despite its lack of direct conversions.
The Research Vs. Conversion Channel Reality
As AI search functions as a research channel, not a conversion channel, this confirms that AI systems are handling awareness and consideration stages, while conversion still requires traditional touchpoints. We found that 34% of AI citations come from PR-influenced sources and 10% from social platforms, demonstrating that traditional SEO concepts like E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) remain critical but must now work at machine scale across multiple platforms.
Immediate CMO Transformation Requirements
Foundation Strengthening: Companies must rapidly enhance SEO fundamentals – structured data, content authority, and technical excellence – that determine whether AI agents can find, understand, and cite their content. Brands not only need to keep the door open to agents, but they also need to embrace them, so they are not invisible to the AI agent processing layer I mentioned earlier.
New Measurement Frameworks: Marketing teams must develop new measurement frameworks that capture AI citation frequency, cross-platform visibility, and influence within AI responses, even when traffic attribution is impossible. Key metrics include brand visibility monitoring, AI presence testing, reference share analysis, and indirect conversion tracking.
CMO And Marketing Team Structure
The team structure evolution reflects a fundamental shift from departmentalized hierarchies to fluid, cross-functional pods. Technical teams become increasingly AI-augmented for scale, content teams shift from creation to curation and refinement, and new integration teams bridge SEO with data science and machine learning departments.
Concluding Thoughts: The CMO, SEO, And AI Reality Check
Here’s the critical takeaway: While you’re optimizing your funnel for AI discovery, remember that organic search is still where conversions happen. AI search serves as the research phase, helping users discover options and gather information.
But when they’re ready to take action – making a purchase, signing up, or downloading – they’re still turning to traditional organic search results. They recognize that AI discovery feeds into the organic funnel. Your SEO foundation becomes the conversion engine that AI discovery feeds into.
The smartest CMOs and marketers aren’t choosing between AI and organic search. They’re using proven SEO strategies as their foundation while adapting for AI discovery.
If you have ever run your writing through a readability checker like Yoast SEO, you have probably come across the Flesch reading score. This metric was developed more than 70 years ago and is still one of the most widely used ways to measure how easy your text is to read. But what does it actually mean, and how does it affect your writing for the web?
In this guide, we will explain how the Flesch reading score works, why it became so prominent in publishing and SEO, and how you can use it effectively today. We will also show you where it fits into the Yoast SEO plugin and why we have introduced new readability checks alongside it.
Table of contents
Reminder: We made some changes to our readability analysis in Yoast SEO 19.3. We replaced the Flesch Reading Ease Score with the word complexity assessment. You can still find the Flesch reading ease score in the Insight tab, but we won’t use this assessment in our readability analysis anymore.
What is the Flesch reading score?
The Flesch reading score, also called the Flesch reading ease test, was created by Rudolf Flesch in the 1940s. His goal was simple: to give writers a quick way of checking whether their text was easy to understand. The formula combines three basic elements: sentence length, word length, and syllable count. When these figures are combined into the formula, which I’ll explain in just a moment, they generate a score between 0 and 100.
The highest scores are reserved for the easiest text. For example, a score in the 90s suggests that a typical 11-year-old child should be able to read it without any difficulty. A score of around 60 is closer to plain English that a high school student would be expected to understand. Scores under 30 are considered very difficult and are only really found in academic or legal writing.
Here’s a quick overview of the ranges and what they mean:
Score range
Readability level
Who can understand it
90–100
Very easy
An average 11-year-old student
80–89
Easy
Middle school students
70–79
Fairly easy
Teenagers aged 13–15
60–69
Standard
High school students
50–59
Fairly difficult
College students
30–49
Difficult
University graduates
0–29
Very confusing
Specialists, academics, or experts
Just for fun: this article itself scores around 63 on the Flesch reading score, which puts it in the “standard” range.
How the Flesch reading score is calculated
The formula behind the score looks intimidating, but don’t worry, it is surprisingly straightforward. In fact, it’s only based on two things. The total number of words divided by the total number of sentences, which gives us the ASL or Average Sentence Length, and the total number of syllables divided by the total number of words to get the ASW or Average Syllables per Word. Once we have these figures, we enter them into this formula:
206.835 – (1.015 × ASL) – (84.6 × ASW)
This will give us a score between 0 and 100. The longer your sentences and the more complex your words, the lower your score will be.
Let’s take a quick example by looking at this short text below:
“The cat sat on the mat. The dog barked.”
This has very short words and sentences, so it would score in the 90s, which means it is very easy to read.
Now compare it with:
“The domesticated feline reclined languidly upon the woven floor covering, while the canine produced a resonant vocalization.”
This is essentially the same meaning, but longer words and clauses drop the score dramatically, likely into the 30s.
This example shows why the Flesch reading score works well as a proxy for readability. It rewards writing that is concise and simple with a high score and wags a finger at writing that is dense and complex, ultimately giving it a low score.
Why the Flesch reading score became important
The Flesch reading score spread beyond classrooms into business and publishing because it answered a universal question: Is my writing easy to understand?
By the 1970s, the U.S. Navy was using it to ensure that training manuals were clear for recruits. Later, several U.S. states made it part of their official requirements for insurance documents and consumer contracts. Healthcare organizations also began using it to ensure that patient information was accessible.
When personal computers became common, Microsoft Word added the Flesch reading ease test to its spelling and grammar tools. Suddenly, anyone writing a school essay or business report could get instant feedback on readability. That mainstreamed the score and kept it relevant well into the digital age.
In the world of web writing, readability became even more critical. Online readers scan rather than study text. Research shows they decide within seconds whether a page is worth their time or not. That makes clarity a competitive advantage. Tools that included the Flesch reading score gave web writers a way to benchmark themselves and improve user experience.
The Flesch reading score in Yoast SEO
When Yoast introduced readability checks to the plugin, the Flesch reading score was one of the first tools we built in. We popularized the use of tools to score your content. It gave writers using WordPress an instant way to measure whether their content was accessible to a broad audience. You can still find the Flesch reading ease score inside the plugin today, in the insights tab.
This has helped thousands of users discover that shorter sentences and simpler words often improve how people engage with their content. While the score does not guarantee better rankings, it does contribute to a positive reading experience, which in turn can influence user behavior and SEO outcomes.
The Insights tab contains a lot of information, including your Flesch reading ease score
Why Yoast moved beyond Flesch
Although the Flesch reading score remains useful, it is not perfect. It only looks at sentence and word length, without considering context, tone, or audience. A blog post aimed at medical professionals may score poorly but still be exactly right for its readers.
That is why we developed additional checks, including word complexity, which evaluates how challenging your vocabulary might be. This allows writers to balance clarity with precision, rather than chasing a single score. In practice, this means you can still use the Flesch reading score as a quick reference, but you should combine it with other insights to get the full picture.
Should you still care about the Flesch reading score?
The Flesch reading score remains a valuable guide for writers who want to make their content more approachable. If your text scores very low, it may be worth shortening sentences or replacing long words with simpler alternatives. But you do not need to obsess over getting a perfect score.
Readability is about more than numbers. Think about your audience, their expectations, and the purpose of your content. Combine the Flesch reading score with other readability signals to create a text that is clear, engaging, and optimized for both humans and search engines.
How to use the Flesch reading ease score to improve your writing
We’ve come to the essential question. How can you use the Flesch score to improve your writing? Well, you write for an audience and know your audience the best. Before writing or editing, consider what kind of texts fit your readers. Do you sell clothes or organize photography workshops? Or do you write for a mom blog or make step-by-step DIYs? Your content should be relatively easy to read in all these cases since you are targeting a broad audience.
However, remember that you do not have to chase a high Flesch reading score at all costs. For example, you may write about complex, specialist topics for a specific, more knowledgeable audience. Or, perhaps you are an academic blogging about your research? It makes sense if the Flesch test produces a lower score in those cases.
Still, whatever your situation is, your text always benefits from concise language. So, if you want to benefit from the feedback the Flesch reading ease score gives you, focus on two things:
1. Shorten your sentences
Too many long sentences make your text difficult to read, while short sentences keep the subject clear. When the sentences in your text are short, you allow your readers to absorb the information in your text. As a result, they don’t need to use all their attention to decipher what you want to say. That is why we advise you to break down long sentences; your text will be much easier to read.
And please, don’t think that by using short sentences, you will oversimplify your text. Let’s compare two short texts to show you what we mean. First, we have this sentence:
My favorite place to visit during weekends is my grandparents’ house near the lake, where we love to fish and swim, and we often take the boat out on the lake.
Did you find this sentence easy to read? Wasn’t it too lengthy, confusing, and difficult to process? Breaking it into two or more sentences can make it much clearer:
My favorite place to visit during weekends is my grandparents’ house. It’s near the lake, where we love to fish and swim. We also often take the boat out on the lake.
These few short sentences are much easier to read. Yet, you give the same information as in the long sentence, so there is no oversimplifying. Using short sentences keeps the subject clear and lets your readers absorb the information you’re presenting.
2. Limit your use of difficult words
Words with four or more syllables are considered difficult to read, so try to avoid them where possible. Or try not to use them too much. For example, try words like small instead of minuscule, about instead of approximately, and use instead of utilize. We have the word complexity assessment in Yoast SEO Premium to help you with that.
If you want to reach a broad audience, you should also try to avoid using jargon. If you’re a medical expert, you’re probably familiar with terms like analgesic, intravenous, and oophorectomy. However, keep in mind that most people aren’t. When you can’t find a better alternative, make sure to explain it for users who might not know the word.
Conclusion
The Flesch reading score has been around for decades, and it is not going anywhere. It still offers a quick way to test whether your writing is easy to follow, and it continues to play a role in Yoast SEO. At the same time, the web has moved on, and so have we. By combining the score with modern checks like word complexity, you can create content that is not only readable but also effective in meeting your goals.
So next time you write a blog post, take a look at your Flesch reading score. Use it as a guide, not a rule. The result will be content that your readers and search engines will thank you for.
TLDR
You should care about your score, but do not chase perfection. Balance readability with your audience’s needs
The Flesch Reading Score measures how easy a text is to read, using sentence length and word length
Scores range from 0 to 100: higher is easier. For example, 90–100 is very easy, 60–69 is standard, and 0–29 is very confusing
It became popular in education, government, and publishing before being integrated into tools like Microsoft Word and SEO platforms
In Yoast SEO, the Flesch reading score still exists in the Insights tab, but we now also use word complexity to provide more accurate feedback
Go Premium and get free access to our SEO courses!
Learn how to write great content for SEO and unlock lots of features with Yoast SEO Premium:
Brendan Reid
Brendan is a seasoned writer with a particular interest in SMEs. What he really enjoys is being able to provide real, actionable steps that can be taken today to start making business better for everyone.
Google Ads continues to shift its focus from keywords to campaign types, such as Performance Max and Demand Gen, making audiences more critical than ever. Google has enhanced its audience targeting capabilities in Display campaigns to resemble those on Meta, LinkedIn, and other social channels.
Custom audiences
Search and Shopping campaigns take advertisers only so far. Display campaigns can help by building top-of-funnel prospects via custom audiences.
A “custom audience” is any group of consumers chosen by an advertiser, such as:
Visitors to a contact-us page who don’t convert,
Email newsletter subscribers,
Searchers with specific needs, such as queries for “electrician services” or “wireless headphones,”
Shoppers who browse a category of websites, such as those similar to ESPN,
Searchers who query “wireless headphone” terms but don’t browse ESPN-like sites.
Depending on the campaign type, Google either targets these audiences or uses them as a signal. For example, a Display campaign would target prospects in “electrician services” for an in-market audience. A Performance Max campaign would use “electrician services” as a signal and show ads to prospects inferred by Google.
Custom audiences are limitless, which is why advertisers should continually test to identify the top performers.
Creating custom audiences
The most common custom audience is visitors to a website — individual pages or combinations. For instance, here are visitors to a product page who did not convert. They viewed the “/products” URL but not the “/thank-you.”
This custom audience is visitors who viewed the “/products” URL but not the “/thank-you.” Click image to enlarge.
Yet targeting is more creative with custom segments, which, like website audiences, are located in the Audience Manager. There are two types of custom segments:
“People with any of these interests or purchase intentions,”
“People who search for any of these terms on Google.”
The options are similar, but “interests” includes websites visited, apps used, and content consumed, versus searchers who query specific terms. Thus “interests” are macro targeting while searches are micro.
Here is a custom interest segment for wireless headphones. I’ve included a few headphone terms and sites that prospects visit.
This custom interest segment includes prospects’ headphone terms and related websites. Click image to enlarge.
I could narrow the segment by excluding prospects interested in Apple AirPods. I could then create a separate segment targeting “AirPod” terms, and even combine the two for a new segment.
Segments can exclude other segments or interests. This example excludes prospects who are interested in AirPods. Click image to enlarge.
Combined segments can include “Interests & detailed demographics,” not just other custom segments. Hence I could add Google-defined headphone users to my combined segment.
Combined segments can include “Interests & detailed demographics,” not just other custom segments. Click image to enlarge.
The result is seemingly endless potential audiences, general and specific, to test across campaign types. Perhaps one custom segment works better as a signal in Performance Max than as a target in Display.
Be creative! That’s the takeaway. Targeting in Google Ads may not be as granular as Meta or LinkedIn, but it is essential for scaling your account.
Pat Reinhart, VP of Services & Thought Leadership at Conductor, and Luiza Shahbazyan, Sr. R&D Product Manager at Conductor, will show you exactly how to win in the age of AI search. You’ll learn:
How to maximize your brand’s visibility across AI answer engines.
Key signalsthat influence AI citations, including content authority and digital PR.
Practical strategies to earn mentions and strengthen trust signals.
How to adapt your SEO workflows for Answer Engine Optimization (AEO).
Reserve Your Spot Today
Register now to get actionable tactics and data-backed insights that help your brand show up in AI results.
🛑 Can’t attend live? Sign up anyway, and we’ll send the full recording straight to your inbox.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Microsoft says AI can create “zero day” threats in biology
A team at Microsoft says it used artificial intelligence to discover a “zero day” vulnerability in the biosecurity systems used to prevent the misuse of DNA.
These screening systems are designed to stop people from purchasing genetic sequences that could be used to create deadly toxins or pathogens. But now researchers say they have figured out how to bypass the protections in a way previously unknown to defenders. Read the full story.
—Antonio Regalado
If you’re interested in learning more about AI and biology, check out:
+ AI-designed viruses are here and already killing bacteria. Read the full story.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1Apple removed an app for reporting ICE officer sightings The US Attorney General requested it take down ICEBlock—and Apple complied. (Insider $) + Apple says the removal was down to the safety risk it posed. (Bloomberg $) + The company had a similar explanation for removing a Hong Kong map app back in 2019. (The Verge)
2 OpenAI’s parental controls are easily circumvented Its alerts about teenagers’ concerning conversations also took hours to deliver. (WP $) + The looming crackdown on AI companionship. (MIT Technology Review)
3 VCs have sunk a record amount into AI startups this year To the tune of $192.7 billion so far. (Bloomberg $) + The AI bubble is looking increasingly precarious, though. (FT $) + How to fine-tune AI for prosperity. (MIT Technology Review)
4 The US federal vaccination schedule is still waiting for an update Officials are yet to sign off on recommendations for this year’s updated Covid shots. (Ars Technica) + Many people have been left unable to get vaccinated. (NPR)
5 The US Department of Energy has canceled yet more clean energy projects In mostly blue states. (TechCrunch) + More than 300 funding awards have been axed. (CNBC) + How to make clean energy progress under Trump in the states. (MIT Technology Review)
6 TikTok recommends pornography to children’s accounts Despite activating its “restricted mode” to prevent sexualized content. (BBC)
7 China has launched a new skilled worker visa program In the wake of the US H-1B visa clampdown. (Wired $) + The initiative hasn’t gone down well with locals. (BBC)
8 Flights were grounded in Germany after several drone sightings NATO members are worried about suspected Russian incursions in their skies. (WSJ $) + It’s the latest in a string of airspace sightings. (FT $)
9 How YouTube is shaking up Hollywood Its powerful creators are starting to worry the entertainment establishment—and Netflix. (FT $)
10 Anti-robocall tools are getting better Call screening features are a useful first line of defense. (NYT $)
Quote of the day
“Capitulating to an authoritarian regime is never the right move.”
—Joshua Aaron, the developer of ICEBlock, the app that crowdsources sightings of ICE officials, hits back at Apple’s decision to remove it from the App Store, 404 Media reports.
One more thing
How AI can help supercharge creativity
Existing generative tools can automate a striking range of creative tasks and offer near-instant gratification—but at what cost? Some artists and researchers fear that such technology could turn us into passive consumers of yet more AI slop.
And so they are looking for ways to inject human creativity back into the process: working on what’s known as co-creativity or more-than-human creativity. The aim is to develop AI tools that augment our creativity rather than strip it from us. Read the full story.
—Will Douglas Heaven
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ Congratulations to Fizz, the very handsome UK cat of the year! + What it took to transform actor Jeremy Allan White into the one and only Boss in his new film, Deliver Me from Nowhere. + Divers have salvaged more than 1,000 gold and silver coins from a 1715 shipwreck off the east coast of Florida. + The internet is obsessed with crabs. But why?
Jon Shanahan destroys the myth that founders make lousy employees. He co-founded Stryx, a men’s cosmetics provider, and is now a marketing executive at TRX, the storied exercise equipment company. He has thrived in both roles.
He joined TRX in late 2022 amid a post-Covid hangover and a stale legacy brand. Fast forward to late 2025, and TRX is refreshed and flourishing, thanks in part to Jon’s entrepreneurial mindset.
While at Stryx, Jon appeared on the podcast twice, in 2020 and 2022. In this latest conversation, he shares transitioning to a large corporation, the challenges of reviving a brand, and more.
Our entire audio is embedded below. The transcript is condensed and edited for clarity.
Eric Bandholz: What are you doing now?
Jon Shanahan: I transitioned to TRX Training as vice president of marketing at the end of 2022. TRX is the global training brand recognized for its distinctive black-and-yellow suspension straps, a bodyweight training system found in nearly every gym.
You helped us at Stryx when we launched into Target in 2022. Stryx and Beardbrand entered during a major aisle reset, which eliminated many existing brands. Four or five other companies launched alongside us, but by 2023, Target had removed all of us — even though our sales exceeded Target’s benchmarks.
TRX filed for bankruptcy in early 2022, following the collapse of the fitness boom. During the pandemic, anything fitness-related was popular, and TRX was everywhere — Nordstrom, REI, Hy-Vee. However, demand eventually dropped, and the company overextended itself.
In August, founder Randy Hetrick reacquired TRX. His goal was to modernize a 20-year-old global brand for a new generation. Initially, I wasn’t keen on moving to Florida from my home in Pennsylvania, but I eventually did, and I joined the team.
My initial focus was brand strategy. I conducted a global study — TRX operates in 80-plus countries — to clarify its identity and market role. That led to a complete refresh, including a new logo. Randy supported it, and it’s been well received.
Soon, I took over all marketing and later expanded into ecommerce and in-store retail, along with TRX’s commercial and education businesses. Unlike Stryx, where I was the face of the brand, here I’m behind the scenes, scaling a legacy brand. TRX had diehard fans, so the challenge is guiding that loyalty into growth and innovation.
Bandholz: You created a new TRX logo. Did it receive a backlash?
Shanahan: Surprisingly, no. In Europe, we have 25 long-time distributors who’ve supported TRX since Randy first sold straps himself. I was intentional in the redesign — it had to feel like TRX but with a modern edge. The heritage mattered, but it needed a fresh approach.
The decision came while we were building a new headquarters in Delray Beach. Seeing the old logo in the renderings, I realized that if we hadn’t changed it then, we never would. It marked a new era for TRX.
The update wasn’t drastic. We retained the iconic “TRX” name and black-and-yellow colors, while refining the design. Rolling it out took time because of our extensive SKUs. We phased it in digitally first, then packaging and straps, keeping costs down.
We also ensured stakeholders were on board. Distributors, retailers, and internal teams were the first to preview it. Notably, the redesign was by the original TRX designer — the same one who worked with Randy in his garage. Bringing him back gave the refresh authenticity.
The reception was smooth. For us, the new logo signaled TRX’s return and future direction.
Bandholz: You oversee retail, direct-to-consumer, and Amazon. How do you prioritize and align those channels?
Shanahan: Each channel requires a different approach. Amazon is a daily knife fight. You need competitive, lower-priced SKUs to stand out. Our ecommerce site, by contrast, is the brand’s showcase. That’s where we feature premium products and position TRX as the leader.
We manage channel conflict with multiple SKUs. For example, we sell 18 versions of the suspension trainer: two premium models on our stie, three “good-better-best” options on Amazon, and value-driven versions in physical retail.
Retail messaging is sport-specific, such as golf, pickleball, and tennis, since shoppers want programs tied to their favorite activities. On Amazon, people mostly search for “home gym” or “home strength,” so we optimize our keywords accordingly.
Our site emphasizes heritage — “the iconic strap” — and certain high-ticket products, such as our 20- and 40-pound weight vests. They wouldn’t sell on Amazon.
Beyond consumer channels, we’re expanding into commercial and educational sectors. That means learning what gyms, trainers, and pros need and then translating those insights back to consumers. After two years, I feel we’ve hit a stride — 2026 will be about strengthening those cross-channels.
Bandholz: You’ve transitioned from an entrepreneurial role at Stryx to a corporate environment.
Shanahan: Founders bring a unique skill set, but the transition isn’t always easy. For those early in their careers, I often recommend starting with an established company. You’ll get paid to make mistakes and learn valuable lessons. I spent years at Apple and a software firm before starting YouTube projects and co-founding Stryx. I can apply those lessons in a corporate role.
Joining an existing team, I leveraged my finance literacy while also focusing on listening. The first six months ideally are dedicated to understanding how things work before making any changes.
Clear communication is critical. I talk daily with leadership to share issues and align on direction, then relay that to the team. It feels like being a founder again — selling the vision, gathering feedback, and building buy-in.
Bandholz: Let’s talk about licensing. How can a brand establish those high-margin collaborations?
Shanahan:Licensing comes in two forms. In-licensing is what we did with The Ohio State University. We created a TRX strap branded with that school’s name, paid royalties, and benefited from the recognition. Out-licensing is the reverse: putting TRX on products we don’t manufacture. For that to work, our brand must carry strong market credibility.
TRX is authentic in functional training, so extending into other training products makes sense. It allows consumers to choose TRX-branded items over generic private-label alternatives at stores such as Dick’s Sporting Goods. That’s a direction we’re exploring for 2026.
We’ve had inbound interest from various companies. For example, Dick’s fitness section features Everlast resistance bands and New Balance jump ropes — products manufactured by third-party companies, which pay a 5–10% royalty. Walmart is similar, with about 60% of its fitness gear being licensed brands and 40% private label.
Bandholz: Where can people follow you, buy some TRX bands?
