Sep 24 2025
Are AI Search Summaries Making Evergreen Articles Obsolete? via @sejournal, @martinibuster

Ahrefs’ Tim Soulo recently posted that AI is making publishing evergreen content obsolete and no longer worth the investment because AI summaries leave fewer clicks for publishers.  He posits that it may be more profitable to focus on trending topics, calling it Fast SEO.  Is publishing evergreen content no longer a viable content strategy?

The Reason For Evergreen Content

Evergreen content can be a basic topic that generally doesn’t change much from year to year. For example, the answer to how to change a tire will generally always be the same.

The promise of evergreen content was that it represents a steady source of traffic. Once a web page is ranking for evergreen topics, publishers basically just have to make sure that it’s updated if the topic has changed in some way.

Does AI Break The Evergreen Content Promise?

Tim Soulo is suggesting that evergreen content, which can be easy to answer with a summary, is less likely to send a click because AI summarizes the answer and satisfies the user, who may not need to visit a website.

Soulo tweeted:

“The era of “evergreen SEO content” is over. We’re entering the era of “fast SEO.”

There’s little point in writing yet another “Ultimate Guide To ___.” Most evergreen topics have already been covered to death and turned into common knowledge. Google is therefore happy to give an AI answer, and searchers are fine with that.

Instead, the real opportunity lies in spotting and covering new trends — or even setting them yourself.”

Is Fast SEO The Future Of Publishing?

Fast SEO is another way of describing trending topics. Trending topics have always been around; it’s why Google invented the freshness algorithm, to satisfy users with up-to-date content when a “query deserves freshness.”

Soulo’s idea is that trending topics are not the kind of content that AI summarizes. Perplexity is the exception; it has an entire content discovery section called Perplexity Discover that’s dedicated to showing trending news articles.

Fast SEO is about spotting and seizing short-lived content opportunities. These can be new developments, shifts in the industry or perceptions, or cultural moments.

His tweet captures the current feeling within the SEO and publishing communities that AI is the reason for diminishing traffic from Google.

The Evergreen Content Situation Is Worse Than Imagined

A technical issue that Soulo didn’t mention but is relevant here is that it’s challenging to create an “Ultimate Guide To X, Y, Z” or the “Definitive Guide To Bla, Bla, Bla” and expect it to be fresh and different from what is already published.

The barrier to entry for evergreen content is higher now than it’s ever been for several reasons:

  • There are more people publishing content.
  • People are consuming multiple forms of content (text, audio, and video).
  • Search algorithms are focused on quality, which shuts out those who focus harder on SEO than they do on people.
  • User behavior signals are more reliable than traditional link signals, and SEOs still haven’t caught on to this, making it harder to rank.
  • Query Fan-Out is causing a huge disruption in SEO.

Why Query Fan-Out Is A Disruption

Evergreen content is an uphill struggle, compounded by the seeming inevitability that AI will summarize the content and, because of Query Fan-Out, possibly send the click to another website that is cited because it offers the answer to a follow-up question to the initial search query.

Query Fan-Out displays answers to the initial query and to follow-up questions to the initial search query. If the user is happy with the summary to the initial query, they may become interested in one of the follow-up queries, and one of those will get the click, not the initial query.

This completely changes what it means to target a search query. How does an SEO target a follow-up question? Maybe, instead of targeting the main high-traffic query, it may make sense to target the follow-up queries with evergreen content.

Evergreen Content Publishing Still Has Life

There is another side to this story, and it’s about user demand. Foundational questions stick around for a long time. People will always search “how to tie a bowtie” or “how to set up WordPress.” Many users prefer the stability of an established guide that has been reviewed and updated by a trusted brand. It’s not about being a brand; it’s about being the kind of site that is trusted, well-liked, and recommended.

A strong resource can become the canonical source for a topic, ranking for years and generating the kind of user behavior signals that reinforce its authority and signal the quality of being trusted.

Trend-driven content, by contrast, often delivers only a brief spike before fading. A newsroom model is difficult to maintain because it requires constant work to be first and be the best.

The Third Way: Do It All

The choice between producing evergreen content and trending topics doesn’t have to be binary; there’s a third option where you can do it all. Evergreen and trending topics can complement each other because each side provides opportunities for driving traffic to the other. Fresh, trend-driven content can link back to the evergreen, and this can be reversed to send readers to fresh content from the evergreen.

Trend-driven content sometimes becomes evergreen itself. But in general, creating evergreen content requires deep planning, quality execution, and marketing. Somebody’s going to get the click from evergreen content, it might as well be you.

Featured Image by Shutterstock/Stokkete

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Sep 23 2025
Pew: Most Americans Want AI Labels, Few Trust Detection via @sejournal, @MattGSouthern

A new Pew Research Center survey reveals a gap between people’s desire to know when AI is used in content and their confidence in being able to identify it.

Seventy-six percent say it’s extremely or very important to know whether pictures, videos, or text were made by AI or by people. Only 12% feel confident they could tell the difference themselves.

Pew Research Center wrote:

“Americans feel strongly that it’s important to be able to tell if pictures, videos or text were made by AI or by humans. Yet many don’t trust their own ability to spot AI-generated content.”

This confidence gap reflects a rising unease with AI.

Half of Americans believe that the increased presence of AI in daily life raises more concerns than excitement, while just 10% are more excited than worried.

What Pew Research Found

People Want More Control

About 60% of Americans want more control over AI in their lives, an increase from 55% last year.

They’re open to AI helping with daily tasks, but still want clarity on where AI ends and human involvement begins.

When People Accept vs. Reject AI

Most support the use of AI in data-intensive tasks, such as weather prediction, financial crime detection, fraud investigation, and drug development.

About two-thirds oppose AI in personal areas such as religious guidance and matchmaking.

Younger Audiences Are More Aware

Awareness of AI is highest among adults under 30, with 62% claiming they’ve heard a lot about it, compared to only 32% of those 65 and older.

But this awareness doesn’t lead to optimism. Younger adults are more likely than seniors to believe that AI will negatively impact creative thinking and the development of meaningful relationships.

Creativity Concerns

More Americans believe AI will negatively impact essential human skills.

Fifty-three percent think it will reduce creative thinking, and 50% feel it will hinder the ability to connect with others, with only a few expecting improvements.

This suggests labeling alone isn’t sufficient. Human input must also be evident in the work.

Why This Matters

People are generally not against AI, but they do want to know when AI is involved. Being open about AI use can help build trust.

Brands that go the transparent route might find themselves at an advantage in creating connections with their audience.

For more insights, see the full report.

Featured Image: Roman Samborskyi/Shutterstock

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Sep 23 2025
SERP Visibility Decline: How To Grow Brand Awareness When Organic Traffic Stalls

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

Text‑heavy AI Overviews blocking your brand?

The “People Also Ask” that you could scroll through forever, effectively hiding position 1?

Knowledge panels and rich snippets hogging the view?

The majority of people who entered a search query never made it past the top of the search result page (SERP) in 2024.

For users, these updates to Google’s SERPs are technically efficient.

For you, changes like AI Overviews are another strategy to master, and at worst, a direct competitor for attention.

So how do you increase brand awareness and search presence when Google is taking away your bids for top-of-funnel (TOFU) content?

The Rise of Zero-Click: Why Rankings Don’t Equal Traffic Anymore

As search evolves, AI-powered summaries now appear in more than 13% of queries.

This resulted in nearly 60% of Google searches ending without a click last year, dramatically shrinking the traditional flow of search traffic to a website.

Not only are you fighting for space against the usual blue links, you’re now competing with AI-generated answers that package everything up before a user even considers a click.

Which means that “we made it to the top” moment doesn’t guarantee anyone actually sees your brand.

So, even if your brand earns a top ranking, it may never translate into visibility. That’s the reality of today’s zero-click environment, and it is what creates the awareness gap — a challenge that every marketer now has to solve.

What Is Zero Click?

A “zero-click” search happens when a user gets their answer directly on the search results page through featured snippets, knowledge panels, or AI-generated overviews without ever clicking through to a website.

For users, it’s fast and convenient. For brands, it means fewer chances for visitors to actually land on your site, even when you’ve earned a top ranking. Think of it as Google (and increasingly, AI) keeping people inside its own ecosystem rather than sending them out to explore yours.

This is where the awareness gap comes in.

What Is The Awareness Gap?

The awareness gap is the space in which your content is seen, but it is not tied to your brand.

Even if your brand appears in these results, you may never see the traditional signals like traffic or time on site that prove your influence. People might recognize your name or absorb part of your story, but that exposure is not reflected in your metrics.

The gap is the difference between being seen and being measured, and closing it requires a new playbook for visibility and recall.

How Zero-Click Reshapes Discovery

The zero-click trend is most disruptive at the very start of the customer journey. Your website used to be Rome; eventually, all roads led there. Now? Fewer and fewer organic roads exist. That means the earliest brand touchpoints are disappearing.

Here’s what that means for marketers today:

  • Fewer chances for discovery. If users never click, they never see your story. All things that shape early perception, such as your messaging, your visuals, your value props, get skipped.
  • SEO loses some steam. While organic optimization still matters for long-term discoverability (hello, LLMs absorbing and citing content), its ability to drive top-of-funnel awareness isn’t what it used to be. In a zero-click world, amazing content may rank, but still never get seen.
  • Competition gets fiercer. If you’ve relied heavily on organic strategies alone, competitors who invest in paid ads are now likely to edge you out. Ads still sit above AI overviews in many results, and that’s prime real estate that’s hard to ignore.
  • Research shifts elsewhere. With crowded SERPs and often confusing AI answers, users are taking their research off of traditional search platforms to other places. Social media, communities, and unowned channels are becoming important sources for educational content that feels clearer and more trustworthy.

Bottom line: the early doors to discovering your brand are closing faster than they’re opening. It takes a new mix of channels to ensure you’re still part of the conversation.

3 Steps to Reclaim Top-of-Funnel Presence

So what’s a marketer to do? Is all hope lost?

Show up where they are still landing: relevant active sites that deliver clear ad space to your target audience.

Advertising offers a direct and reliable solution to the awareness gap.

Unlike organic results, paid campaigns guarantee an immediate and prominent presence on SERPs and other digital platforms. That means eyeballs on your ads, even if a user doesn’t click on them.

Consider paid campaigns as a type of insurance policy against brand invisibility on the SERP.

Remember: early impressions = stronger recall later in the funnel. The power of showing up first cannot be overstated. Even if a user doesn’t click on your ad, the exposure to your name, logo, or key message fosters familiarity. Early recognition makes your brand more memorable when it comes time to convert.

Step 1: Implement An Awareness-Focused Advertising Strategy

If you’ve made it this far, you’re likely nodding along: zero-click is here, and advertising has to play a bigger role. But where do you start? The good news is you don’t need to overhaul everything overnight. Instead, think of paid as a strategic layer that enhances the visibility you’ve already worked hard to build organically.

Here’s the first step in making that shift in a way that feels purposeful, not scattered:

Leverage common queries

Run search and display ads tied to common zero-click queries. Many of the searches most impacted by zero-click are informational: “what is,” “how to,” and “why does” questions that rarely result in clicks. Instead of letting that traffic disappear into AI overviews, run search and display campaigns against these queries. Your brand may not get the click, but it will get the visibility, ensuring you stay part of the conversation even when Google is trying to keep people on the page.

Connect with tomorrow’s customers today. AdRoll makes brand awareness ads work for you. Get started with a demo.

Use what you already know

Build awareness campaigns in categories where your brand already shows up. If you’ve earned a featured snippet or knowledge panel, don’t leave it unsupported. Pair that organic placement with a targeted ad so your brand appears twice on the same page. This kind of overlap creates a halo effect: users perceive your brand as both authoritative and unavoidable. It’s one of the fastest ways to reinforce recall.

Enhance, don’t replace SEO

Paid advertising isn’t a substitute for strong organic presence, it’s an amplifier. Use ads to reinforce your authority and extend the reach of your organic work, not cover for it. Think of the two channels as partners: SEO earns you credibility, while ads guarantee visibility. Together, they create a more holistic visibility strategy that keeps you top of mind across formats and touchpoints. And don’t forget: LLMs and AI overviews are still learning from organic signals. If your content isn’t strong, your ads won’t carry the same weight.

At the end of the day, this isn’t about abandoning what has always worked. It’s about making sure your brand shows up where discovery is actually happening, whether that’s in a blue link, a snippet, or a sponsored placement.

Step 2: Measure Zero-Click Strategies The Right Way

Here’s the tricky part: in a zero-click world, traditional metrics don’t always tell the whole story. If you’re only watching organic traffic, it may look like your efforts are failing. But the reality is that influence is happening upstream, before a user ever lands on your site.

Here’s what to measure instead:

  • Branded search volume. If more people are searching for your brand name specifically, you know your awareness strategy is working. This is often the clearest leading indicator of recall.
  • Visibility share. Track how often your brand appears in SERPs, featured snippets, AI overviews, and paid placements, even if it doesn’t result in a click.
  • Impression lift. Ads may not drive immediate conversions, but consistent exposure increases recognition. Measuring impressions alongside recall surveys can help connect the dots.
  • Engagement on unowned channels. As research moves to social and communities, track where your educational content sparks conversations and shares outside of your own site.

The key is to shift from measuring traffic to measuring presence. Visibility in high-authority spaces, whether through organic or paid efforts, is the new top-of-funnel KPI.

Step 3: Connect The C-Suite To Zero-Click Strategies

Of course, metrics only matter if your leadership team understands them. However, many executives are still trained to see organic traffic as the gold standard. So when traffic dips, even for reasons outside your control, it can look like a problem.

This is where your role as translator becomes critical. You need to reframe the conversation from clicks to visibility, from pageviews to presence. The message to the C-suite should sound less like an apology and more like a strategic shift:

  • A decline in organic traffic doesn’t equal a decline in influence. Zero-click means users may never land on your site, but they’re still seeing your brand. Visibility is impact.
  • Your brand may actually be showing up more often. The problem is measurement, not presence. Snippets, AI overviews, and social conversations don’t show up in traffic charts, but they absolutely shape perception.
  • Advertising fills the gap. Paid campaigns guarantee your brand isn’t invisible at the exact moment prospects are forming their first impressions, making it the perfect complement to organic efforts.

The way to make this stick with leaders is through narrative. Show them that early impressions are building brand memory. Connect branded search growth to that recall. Paint the picture that what looks like “less traffic” is often “more visibility in new places.”

Executives care about competitive positioning and long-term growth, not just line graphs. So remind them: being the brand people remember when it’s time to buy is the real win. Presence is what creates that memory, and memory is what drives future pipeline.

Zero-Click Isn’t the End. It’s Your Advantage If You Move First

Zero-click isn’t the end of marketing as we know it. It’s just the latest evolution in how people discover and remember brands. The marketers who win will be the ones who adapt their strategies, blending organic authority with paid presence, reframing their KPIs, and helping their companies understand what visibility really means today.

The awareness gap is real, but it’s also an opportunity. By rethinking how you measure, how you communicate results, and how you show up at the top of the funnel, you can set your brand up to thrive in an environment where discovery no longer depends on a click.

And this is only Part 1. In Part 2, we’ll dig into the real secret weapon in a clickless world: recall. Because the brands that stay top of mind are the ones that get chosen later. Advertising’s biggest power isn’t in driving a click, it’s in building the kind of recognition that lasts.

Check back soon on the AdRoll website for Part 2: How to Build Recall in a Clickless World.

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Image Credits

Featured Image: Image by AdRoll. Used with permission.

In-Post Images: Image by AdRoll. Used with permission.

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Sep 22 2025
Agentic AI In SEO: AI Agents & The Future Of Content Strategy (Part 3) via @sejournal, @VincentTerrasi

For years, the SEO equation appeared to be a fixed and unchanging landscape: optimizing for Googlebot on one side, and creating content for human users on the other. This outdated binary vision is now a thing of the past.

In the current business environment, a new generation of actors is causing significant changes to the online visibility landscape. AI agents such as ChatGPT, Perplexity, Claude, and Gemini are no longer merely processing information; they are exploring, synthesizing, choosing sources to cite, and significantly influencing traffic flows.

For those who are skeptical about the impact of AI agents, I would invite you to consider the concept of Zero Moment of Truth (ZMOT), which was developed by Google over 10 years ago. The principle is straightforward: Prior to any purchase, consumers undertake an extensive research phase. They consult customer reviews, compare across different sites, scrutinize social networks, accumulate information sources, and now use their favorite AIs for final validation.

A New Paradigm

We are currently experiencing a fundamental reconfiguration of the digital ecosystem. In the past, we have identified two or three main engines. However, a new paradigm is emerging.

Google continues to be a leading search engine, utilizing sophisticated algorithms to index and rank content. Humans act as a virality engine, sharing and amplifying information via their social networks and interactions.

It is becoming increasingly apparent that AI agents are assuming the role of an autonomous traffic engine. These intelligent systems are capable of navigating information independently, establishing their own selection criteria, and directing users to sources they deem relevant.

This transformation necessitates a wholly new approach to content creation, which I will be sharing imminently. I will be sharing concepts and case studies that have been successfully implemented with several major accounts.

Agentic SEO

Quick reminder following my two previous articles on the subject: “Agentic AI In SEO: AI Agents & Workflows For Ideation (Part 1)” and “Agentic AI In SEO: AI Agents & Workflows For Audit (Part 2).”

Agentic SEO involves the creation of structured and dynamic content that is designed to appeal not only to Google, but also to conversational AIs.

The approach to content generation is founded on three key pillars:

1. Data Enrichment: Schema.org data, microformats, and semantic tags are becoming important as, when grounding data, they can facilitate understanding and information extraction by language models.

2. Content Modularity: Concise and “chunkable” responses are perfectly suited to Retrieval-Augmented Generation (RAG) ingestion processes utilized by these agents. Content should be designed using autonomous and reusable blocks.

3. Polymorphism: Each page can offer variants adapted according to the type of agent consulting it. It is essential to recognize that the needs of a shopping agent differ from those of a medical agent, and content must adapt accordingly.

Image from author, September 2025

If your content isn’t optimized for AI agents, you’re already experiencing considerable strategic lag.

However, if your site is optimized for SEO, you’ve already taken a significant step forward.

The Foundations: Generative SEO And Edge SEO

To understand this evolution, it is important to consider the concepts that have prepared the ground: generative SEO and Edge SEO.

Generative SEO

Generative SEO facilitates the creation of substantial and insightful content through the utilization of language models. This approach automates the process of creating content while ensuring its relevance and quality.

Generative SEO has always existed in primitive forms, such as content spinning and all derived techniques. In today’s digital landscape, we are witnessing a paradigm shift towards unparalleled quality, as evidenced by the preponderance of AI-generated or co-written content across various social networks, including LinkedIn.

Edge SEO

Edge SEO leverages CDN or proxy-side deployment capabilities to reduce deployment latency and enable large-scale content testing from both content and performance perspectives.

These two approaches are naturally complementary, but they still represent a 1.0 vision of automated SEO. It is important to note that traditional A/B testing and content freezing, once generation is complete, limit the potential of the project.

The true revolution lies in the adoption of dynamic and adaptive systems that surpass these limitations.

Agentic Edge SEO

Edge SEO had already revolutionized the very notion of static content. The system now has the capability to modify content in real-time according to the following three variables:

  • Firstly, user intention is detected and used to guide content adaptation. The system is able to analyze behavioral signals in order to adjust the message in real-time.
  • Next, let us consider the impact of SERP seasonality on modifications. When Google prioritizes certain trends on a given query, content automatically adapts to capitalize on these evolutions.
  • Finally, the instant technical optimizations triggered by Core Web Vitals signals ensure that performance is maintained.
Image from author, September 2025

Let us consider a product page as a case study. If Google highlights “sustainable” or “economical” trends for a particular search, this page automatically adapts its titles, metadata, and visuals to align with these market signals.

At Draft&Goal, we have developed connectors with the Fasterize tool to facilitate the deployment of AI workflows. These workflows are compatible with all the most recent proprietary or open-source LLMs.

We anticipate that in the future, the system will continuously test these variants with search engines and users, collecting performance data in near real-time.

The most effective version is then selected by the algorithm, in terms of click-through rate (CTR), positioning, and conversion, with results continually being optimized.

For example, imagine a “Running Shoes” landing page, existing in seven distinct versions, each oriented towards a specific angle: price, performance, comfort, ecology, style, durability, or innovation. The polymorphic system automatically highlights the most effective variant according to signals sent by Google and user behaviors.

Three Concrete Applications

These concepts are immediately applicable to several strategic sectors. Allow me to provide three examples of the products currently under active testing.

In ecommerce, product pages are self-evolving. These systems adapt to search trends, available stock, and detected behavioral preferences.

1. To illustrate this point, consider a peer-to-peer car rental platform that manages 20,000 city pages.

Each page automatically adapts according to Google signals and local user patterns. During the summer months, the “Car rental Nice” page automatically prioritizes convertibles and highlights family testimonials. During the winter season, the fleet is transitioned to 4×4 vehicles, with a focus on optimizing the “mountain car rental” service.

2. Another example of technological innovation in the media industry is the ability of major news outlets to deploy “living” articles.

These articles are automatically updated to include the latest breaking news, ensuring that content remains fresh and relevant without the need for human editorial intervention. We continue to prioritize content creation by human professionals, with AI playing a supportive role in maintaining currency.

3. Finally, the promo codes website has successfully managed 3,000 merchant pages, which adapt in real-time to commercial cycles and breaking deals.

Amazon’s Prime Days announcement is met with the automatic enrichment of contextual banners and temporal counters on all related pages. The system is designed to monitor partner APIs in order to detect new offers and instantly generate optimized content. Three weeks before Black Friday, “Zalando promo codes” pages automatically integrate dedicated sections and restructure their keywords.

Toward A New Era Of SEO

The future of SEO lies in publishing dynamic content that can adapt to the ever-changing algorithms of Google’s index. This transformation requires a fundamental paradigm shift, and many SEO agencies we support have already made the switch.

Marketing experts must abandon the “page” logic to adopt that of “adaptive systems.” This transition necessitates the acquisition of new tools and skills, as well as a re-evaluation of our strategic vision.

It is important to note that Agentic SEO is not merely a passing trend; it is the necessary response to an ecosystem undergoing profound mutation. Organizations that master these concepts will gain a significant competitive advantage in tomorrow’s attention economy.

More Resources:

Featured Image: Collagery/Shutterstock

Ecommerce MGMT 0 Comments
Sep 18 2025
Google Brings AI Mode To Chrome’s Address Bar via @sejournal, @MattGSouthern

Google is rolling out AI Mode to the address bar in Chrome for U.S. users.

This move is part of a series of AI updates, including Gemini in Chrome, page-aware question prompts, improved scam protection, and instant password changes.

See Google’s launch video below:

What’s New

Google Chrome will enable you to access AI Mode directly from the search bar on desktop, ask follow-up questions, and explore the web more in-depth.

Additionally, Google is introducing contextual prompts that are connected to the page you’re currently viewing. When you use these prompts, an AI Overview will appear on the right side of the screen, allowing you to continue using AI Mode without leaving the page.

For now, this feature is available in English in the U.S., with plans to expand internationally.

Gemini In Chrome

Gemini in Chrome is rollout out to to Mac and Windows users in the U.S.

You can ask it to clarify complex information across multiple tabs, summarize open tabs, and consolidate details into a single view.

With integrations with Calendar, YouTube, and Maps, you can jump to a specific point in a video, get location details, or set meetings without switching tabs.

Google plans to add agentic capabilities in the coming months. Gemini will be able to perform tasks for you on the web, such as booking appointments or placing orders, with the option to stop it at any time.

Regarding availability, Google notes that business access will be available “in the coming weeks” through Workspace with enterprise-grade protections.

Security Enhancements

Enhanced protection in Safe Browsing now uses Gemini Nano to detect tech-support-style scams, making browsing safer. Google is also working on extending this protection to block fake virus alerts and fake giveaways.

Chrome is using AI to help reduce annoying spammy site notifications and to lower the prominence of intrusive permission prompts.

Additionally, Chrome will soon serve as a password helper, automatically changing compromised passwords with a single click on supported sites.

Why This Matters

Adding AI Mode to the omnibox makes it easier to ask conversational questions and follow-ups.

Content that answers related questions and compares options side by side may align better with these types of searches. Page-aware prompts also create new ways to explore related topics from article pages, which could change how people click through to other content.

Looking Ahead

Google frames this as “the biggest upgrade to Chrome in its history,” with staged rollouts and more countries and languages to come.

Featured Image: Photo Agency / Shutterstock

Ecommerce MGMT 0 Comments
Sep 16 2025
Personas Are Critical For AI search via @sejournal, @Kevin_Indig

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Here’s what I’m covering this week: How to build user personas for SEO from data you already have on hand.

You can’t treat personas as a “brand exercise” anymore.

In the AI-search era, prompts don’t just tell you what users want; they reveal who’s asking and under what constraints.

If your pages don’t match the person behind the query and connect with them quickly – their role, risks, and concerns they have, and the proof they require to resolve the intent – you’re likely not going to win the click or the conversion.

It’s time to not only pay attention and listen to your customers, but also optimize for their behavioral patterns.

Search used to be simple: queries = intent. You matched a keyword to a page and called it a day.

Personas were a nice-to-have, often useful for ads and creative or UX decisions, but mostly considered irrelevant by most to organic visibility or growth.

Not anymore.

Longer prompts and personalized results don’t just express what someone wants; they also expose who they are and the constraints they’re operating under.

AIOs and AI chats act as a preview layer and borrow trust from known brands. However, blue links still close when your content speaks to the person behind the prompt.

If that sounds like hard work, it is. And it’s why most teams stall implementing search personas across their strategy.

  • Personas can feel expensive, generic, academic, or agency-driven.
  • The old persona PDFs your brand invested in 3-5 years ago are dated – or missing entirely.
  • The resources, time, and knowledge it takes to build user personas are still significant blockers to getting the work done.

In this memo, I’ll show you how to build lean, practical, LLM-ready user personas for SEO – using the data you already have, shaped by real behavioral insights – so your pages are chosen when it counts.

While there are a few ways you could do this, and several really excellent articles out there on SEO personas this past year, this is the approach I take with my clients.

Most legacy persona decks were built for branding, not for search operators.

They don’t tell your writers, SEOs, or PMs what to do next, so they get ignored by your team after they’re created.

Mistake #1: Demographics ≠ Decisions

Classic user personas for SEO and marketing overfocused on demographics, which can give some surface-level insights into stereotypical behavior for certain groups.

But demographics don’t necessarily help your brand stand out against your competitors. And demographics don’t offer you the full picture.

Mistake #2: A Static PDF Or Shared Doc Ages Fast

If your personas were created once and never reanalyzed or updated again, it’s likely they got lost in G: Drive or Dropbox purgatory.

If there’s no owner working to ensure they’re implemented across production, there’s no feedback loop to understand if they’re working or if something needs to change.

Mistake #3: Pretty Delivered Decks, No Actionable Insights

Those well-designed persona deliverables look great, but when they aren’t tied to briefs, citations, trust signals, your content calendar, etc., they end up siloed from production. If a persona can’t shape a prompt or a page, it won’t shape any of your outcomes.

In addition to the fact classic personas weren’t built to implement across your search strategy, AI has shifted us from optimizing for intent to optimizing for identity and trust. In last week’s memo I shared the following:

The most significant, stand-out finding from that study: People use AI Overviews to get oriented and save time. Then, for any search that involves a transaction or high-stakes decision-making, searchers validate outside Google, usually with trusted brands or authority domains.

Old world of search optimization: Queries signaled intent. You ranked a page that matched the keyword and intent behind it, and your brand would catch the click. Personas were optional.

New world of search optimization: Prompts expose people, and AI changes how we search. Marketers aren’t just optimizing for search intent or demographics; we’re also optimizing for behavior.

Long AI prompts don’t just say what the user intends – they often reveal who is asking and what constraints or background of knowledge they bring.

For example, if a user prompts ChatGPT something like “I’m a healthcare compliance officer at a mid-sized hospital. Can you draft a checklist for evaluating new SaaS vendors, making sure it covers HIPAA regulations and costs under $50K a year,” then ChatGPT would have background information about the user’s general compliance needs, budget ceilings, risk tolerance, and preferred content formats.

AI systems then personalize summaries and citations around that context.

If your content doesn’t meet the persona’s trust requirements or output preference, it won’t be surfaced.

What that means in practice:

  • Prompts → identity signals. “As a solo marketer on a $2,000 budget…” or “for EU users under GDPR…” = role, constraints, and risk baked into the query.
  • Trust beats length. Classic search results are clicked on, but only when pages show the trust scaffolding a given persona needs for a specific query.
  • Format matters. Some personas want TL;DR and tables; others need demos, community validation (YouTube/Reddit), or primary sources.

So, here’s what to do about it.

You don’t need a five or six-figure agency study (although those are nice to have).

You need:

  • A collection of your already-existing data.
  • A repeatable process, not a static file.
  • A way to tie personas directly into briefs and prompts.

Turning your own existing data into usable user personas for SEO will equip you to tie personas directly to content briefs and SEO workflows.

Before you start collecting this data, set up an organized way to store it: Google Sheets, Notion, Airtable – whatever your team prefers. Store your custom persona prompt cards there, too, and you can copy and paste from there into ChatGPT & Co. as needed.

The work below isn’t for the faint of heart, but it will change how you prompt LLMs in your AI-powered workflows and your SEO-focused webpages for the better.

  1. Collect and cluster data.
  2. Draft persona prompt cards.
  3. Calibrate in ChatGPT & Co.
  4. Validate with real-world signals.

You’re going to mine several data sources that you already have, both qualitative and quantitative.

Keep in mind, being sloppy during this step means you will not have a good base for an “LLM ready” persona prompt card, which I’ll discuss in Step 2.

Attributes to capture for an “LLM-ready persona”:

  • Jobs-to-be-done (top 3).
  • Role and seniority.
  • Buying triggers + blockers (think budget, IT/legal constraints, risk).
  • 10-20 example questions at TOFU, MOFU, BOFU stages.
  • Trust cues (creators, domains, formats).
  • Output preferences (depth, format, tone).

Where AIO validation style data comes in:

Last week, we discussed four distinct AIO intent validations verified within the AIO usability study: Efficiency-first/Trust-driven/Comparative/Skeptical rejection.

If you want to incorporate this in your persona research – and I’d advise that you should – you’re going to look for:

  • Hesitation triggers across interactions with your brand: What makes them pause or refine their question (whether on a sales call or a heat map recording).
  • Click-out anchors: Which authority brands they use to validate (PayPal, NIH, Mayo Clinic, Stripe, KBB, etc.); use Sparktoro to find this information.
  • Evidence threshold: What proof ends hesitation for your user or different personas? (Citations, official terminology, dated reviews, side-by-side tables, videos).
  • Device/age nuance: Younger and mobile users → faster AIO acceptance; older cohorts → blue links and authority domains win clicks.

Below, I’ll walk you through where to find this information.

Qualitative Inputs

1. Your GSC queries hold a wealth of info. Split by TOFU/MOFU/BOFU, branded vs non-branded, and country. Then, use a regex to map question-style queries and see who’s really searching at each stage.

Below is the regex I like to use, which I discussed in Is AI cutting into your SEO conversions?. It also works for this task:

(?i)^(who|what|why|how|when|where|which|can|does|is|are|should|guide|tutorial|course|learn|examples?|definition|meaning|checklist|framework|template|tips?|ideas?|best|top|list(?:s)?|comparison|vs|difference|benefits|advantages|alternatives)b.*

2. On-Site Search Logs. These are the records of what visitors type into your website’s own search bar (not Google).

Extract exact phrasing of problems and “missing content” signals (like zero results, refined searches, or high exits/no clicks).

Plus, the wording visitors use reveals jobs-to-be-done, constraints, and vocabulary you should mirror on the page. Flag repeat questions as latent questions to resolve.

3. Support Tickets, CRM Notes, Win/Loss Analysis. Convert objections, blockers, and “how do I…” threads into searchable intents and hesitation themes.

Mine the following data from your records:

  • Support: Ticket titles, first message, last agent note, resolution summary.
  • CRM: Opportunity notes, metrics, decision criteria, lost-reason text.
  • Win/Loss: Objection snapshots, competitor cited, decision drivers, de-risking asks.
  • Context (if available): buyer role, segment (SMB/MM/ENT), region, product line, funnel stage.

Once gathered, compile and analyze to distill patterns.

Qualitative Inputs

1. Your sales calls and customer success notes are a wealth of information.

Use AI to analyze transcripts and/or notes to highlight jobs-to-be-done, triggers, blockers, and decision criteria in your customer’s own words.

2. Reddit and social media discussions.

This is where your buyers actually compare options and validate claims; capture the authority anchors (brands/domains) they trust.

3. Community/Slack spaces, email newsletter replies, article comments, short post-purchase or signup surveys.

Mine recurring “stuck points” and vocabulary you should mirror. Bucket recurring themes together and correlate across other data.

Pro tip: Use your topic map as the semantic backbone for all qualitative synthesis – discussed in depth in how to operationalize topic-first SEO. You’d start by locking the parent topics, then layer your personas as lenses: For each parent topic, fan out subtopics by persona, funnel stage, and the “people × problems” you pull from sales calls, CS notes, Reddit/LinkedIn, and community threads. Flag zero-volume/fringe questions on your map as priorities; they deepen authority and often resolve the hesitation themes your notes reveal.

After clustering pain points and recurring queries, you can take it one step further to tag each cluster with an AIO pattern by looking for:

  • Short dwell + 0–1 scroll + no refinements → Efficiency-first validations.
  • Longer dwell + multiple scrolls + hesitation language + authority click-outs → Trust-driven validations.
  • Four to five scrolls + multiple tabs (YouTube/Reddit/vendor) → Comparative validations.
  • Minimal AIO engagement + direct authority clicks (gov/medical/finance) → Skeptical rejection.

Not every team can run a full-blown usability study of the search results for targeted queries and topics, but you can infer many of these behavioral patterns through heatmaps of your own pages that have strong organic visibility.

2. Draft Persona Prompt Cards

Next up, you’ll take this data to inform creating a persona card.

A persona card is a one-page, ready-to-go snapshot of a target user segment that your marketing/SEO team can act on.

Unlike empty or demographic-heavy personas, a persona card ties jobs-to-be-done, constraints, questions, and trust cues directly to how you brief pages, structure proofs, and prompt LLMs.

A persona card ensures your pages and prompts match identity + trust requirements.

What you’re going to do in this step is convert each data-based persona cluster into a one-pager designed to be embedded directly into LLM prompts.

Include input patterns you expect from that persona – and the output format they’d likely want.

Optimizing Prompt Selection for Target Audience Engagement

Reusable Template: Persona Prompt Card

Drop this at the top of a ChatGPT conversation or save as a snippet.

This is an example template below based on the Growth Memo audience specifically, so you’ll need to not only modify it for your needs, but also tweak it per persona.

You are Kevin Indig advising a [ROLE, SENIORITY] at a [COMPANY TYPE, SIZE, LOCATION].

Objective: [Top 1–2 goals tied to KPIs and timeline]

Context: [Market, constraints, budget guardrails, compliance/IT notes]

Persona question style: [Example inputs they’d type; tone & jargon tolerance] 

Answer format:

- Start with a 3-bullet TL;DR.

- Then give a numbered playbook with 5-7 steps.

- Include 2 proof points (benchmarks/case studies) and 1 calculator/template.

- Flag risks and trade-offs explicitly.

- Keep to [brevity/depth]; [bullets/narrative]; include [table/chart] if useful.

What to avoid: [Banned claims, fluff, vendor speak] 

Citations: Prefer [domains/creators] and original research when possible.

Example Attribute Sets Using The Growth Memo Audience

Use this card as a starting point, then fill it with your data.

Below is an example of the prompt card with attributes filled for one of the ideal customer profiles (ICP) for the Growth Memo audience.

You are Kevin Indig advising an SEO Lead (Senior) at a Mid-Market B2B SaaS (US/EU).

Objective: Protect and grow organic pipeline in the AI-search era; drive qualified trials/demos in Q4; build durable topic authority.

Context: Competitive category; CMS constraints + limited Eng bandwidth; GDPR/CCPA; security/legal review for pages; budget ≤ $8,000/mo for content + tools; stakeholders: VP Marketing, Content Lead, PMM, RevOps.

Persona question style: “How do I measure topic performance vs keywords?”, “How do I structure entity-based internal linking?”, “What KPIs prove AIO exposure matters?”, “Regex for TOFU/MOFU/BOFU?”, “How to brief comparison pages that AIO cites?” Tone: precise, low-fluff, technical.

AIO validation profile:

- Dominant pattern(s): Trust-driven (primary), Comparative (frameworks/tools); Skeptical for YMYL claims.

- Hesitation triggers: Black-box vendor claims; non-replicable methods; missing citations; unclear risk/effort.

- Click-out anchors: Google Search Central & docs, schema.org, reputable research (Semrush/Ahrefs/SISTRIX/seoClarity), Pew/Ofcom, credible case studies, engineering/product docs.

- SERP feature bias: Skims AIO/snippets to frame, validates via organic authority + primary sources; uses YouTube for demos; largely ignores Ads.

- Evidence threshold: Methodology notes, datasets/replication steps, benchmarks, decision tables, risk trade-offs.

Answer format:

- Start with a three-bullet TL;DR.

- Then give a numbered playbook with 5-7 steps.

- Include 2 proof points (benchmarks/case studies) and 1 calculator/template.

- Flag risks and trade-offs explicitly.

- Keep to brevity + bullets; include a table/chart if useful.

Proof kit to include on-page:

Methodology & data provenance; decision table (framework/tool choice); “best for / not for”; internal-linking map or schema snippet; last-reviewed date; citations to Google docs/primary research; short demo or worksheet (e.g., Topic Coverage Score or KPI tree).

What to avoid:

Vendor-speak; outdated screenshots; cherry-picked wins; unverifiable stats; hand-wavy “AI magic.”

Citations:

Prefer Google Search Central/docs, schema.org, original studies/datasets; reputable tool research (Semrush, Ahrefs, SISTRIX, seoClarity); peer case studies with numbers.

Success signals to watch:

Topic-level lift (impressions/CTR/coverage), assisted conversions from topic clusters, AIO/snippet presence for key topics, authority referrals, demo starts from comparison hubs, reduced content decay, improved crawl/indexation on priority clusters.

Your goal here is to prove the Persona Prompt Cards actually produce useful answers – and to learn what evidence each persona needs.

Create one Custom Instruction profile per persona, or store each Persona Prompt Card as a prompt snippet you can prepend.

Run 10-15 real queries per persona. Score answers on clarity, scannability, credibility, and differentiation to your standard.

How to run the prompt card calibration:

  • Set up: Save one Prompt Card per persona.
  • Eval set: 10-15 real queries/persona across TOFU/MOFU/BOFU stages, including two or three YMYL or compliance-based queries, three to four comparisons, and three or four quick how-tos.
  • Ask for structure: Require TL;DR → numbered playbook → table → risks → citations (per the card).
  • Modify it: Add constraints and location variants; ask the same query two ways to test consistency.

Once you run sample queries to check for clarity and credibility, modify or upgrade your Persona Card as needed: Add missing trust anchors or evidence the model needed.

Save winning outputs as ways to guide your briefs that you can paste into drafts.

Log recurring misses (hallucinated stats, undated claims) as acceptance checks for production.

Then, do this for other LLMs that your audience uses. For instance, if your audience leans heavily toward using Perplexity.ai, calibrate your prompt there also. Make sure to also run the prompt card outputs in Google’s AI Mode, too.

Watch branded search trends, assisted conversions, and non-Google referrals to see if influence shows up where expected when you publish persona-tuned assets.

And make sure to measure lift by topic, not just per page: Segment performance by topic cluster (GSC regex or GA4 topic dimension). Operationalizing your topic-first seo strategy discusses how to do this.

Keep the following in mind when reviewing real-world signals:

  • Review at 30/60/90 days post-ship, and by topic cluster.
  • If Trust-driven pages show high scroll/low conversions → add/upgrade citations and expert reviews and quotes.
  • If Comparative pages get CTR but low product/sales demos signups → add short demo video, “best for / not for” sections, and clearer CTAs.
  • If Efficiency-first pages miss lifts in AIO/snippets → tighten TL;DR, simplify tables, add schema.
  • If Skeptical-rejection-geared pages yield authority traffic but no lift → consider pursuing authority partnerships.
  • Most importantly: redo the exercise every 60-90 days and match your new against old personas to iterate toward the ideal.

Building user personas for SEO is worth it, and it can be doable and fast by using in-house data and LLM support.

I challenge you to start with one lean persona this week to test this approach. Refine and expand your approach based on the results you see.

But if you plan to take this persona-building project on, avoid these common missteps:

  • Creating tidy PDFs with zero long-term benefits: Personas that don’t specify core search intents, pain points, and AIO intent patterns won’t move behavior.
  • Winning every SERP feature: This is a waste of time. Optimize your content for the right surface for the dominant behavioral patterns of your target users.
  • Ignoring hesitation: Hesitation is your biggest signal. If you don’t resolve it on-page, the click dies elsewhere.
  • Demographics over jobs-to-be-done: Focusing on characteristics of identity without incorporating behavioral patterns is the old way.

Featured Image: Paulo Bobita/Search Engine Journal

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Sep 15 2025
ChatGPT Study: 1 In 4 Conversations Now Seek Information via @sejournal, @MattGSouthern

New research from OpenAI and Harvard finds that “Seeking Information” messages now account for 24% of ChatGPT conversations, up from 14% a year earlier.

This is an NBER working paper (not peer-reviewed), based on consumer ChatGPT plans only, and the study used privacy-preserving methods where no human read user messages.

The working paper analyzes a representative sample of about 1.1 million conversations from May 2024 through June 2025.

By July, ChatGPT reached more than 700 million weekly active users, sending roughly 2.5 billion messages per day, or about 18 billion per week.

What People Use ChatGPT For

The three dominant topics are Practical Guidance, Seeking Information, and Writing, which together account for about 77% of usage.

Practical Guidance remains around 29%. Writing declined from 36% to 24% over the past year. Seeking Information grew from 14% to 24%.

The authors write that Seeking Information “appears to be a very close substitute for web search.”

Asking vs. Doing

The paper classifies intent as Asking, Doing, or Expressing.

About 49% of messages are Asking, 40% are Doing, and 11% are Expressing.

Asking messages “are consistently rated as having higher quality” than the other categories, based on an automated classifier and user feedback.

Work vs. Personal Use

Non-work usage rose from 53% in June 2024 to 73% in June 2025.

At work, Writing is the top use case, representing about 40% of work-related messages. Education is a major use: 10% of all messages involve tutoring or teaching.

Coding And Companionship

Only 4.2% of messages are about computer programming, and 1.9% concern relationships or personal reflection.

Who’s Using It

The study documents rapid global adoption.

Early gender gaps have narrowed, with the share of users having typically feminine names rising from 37% in January 2024 to 52% in July 2025.

Growth in the lowest-income countries has been more than four times that of the highest-income countries.

Why This Matters

If a quarter of conversations are information-seeking, some queries that would have gone to search may go toward conversational tools.

Consider responding to this shift with content that answers questions, while adding expertise that a chatbot can’t replicate. Writing and editing account for a large share of work-related use, which aligns with how teams are already folding AI into content workflows.

Looking Ahead

ChatGPT is becoming a major destination for finding information online.

In addition to the shift toward finding info, it’s worth highlighting that 70% of ChatGPT use is personal, not professional. This means consumer habits are changing broadly.

As this technology grows, it’ll be vital to track how your audience uses AI tools and adjust your content strategy to meet them where they are.

Featured Image: Photo Agency/Shutterstock

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Sep 15 2025
When Advertising Shifts To Prompts, What Should Advertisers Do? via @sejournal, @siliconvallaeys

When I last wrote about Google AI Mode, my focus was on the big differentiators: conversational prompts, memory-driven personalization, and the crucial pivot from keywords to context.

As we see with the Q2 ad platform financial results below, this shift is rapidly reshaping performance advertising. While AI Mode means Google has to rethink how it makes money, it forces us advertisers to rethink something even more fundamental: our entire strategy.

In the article about AI Mode, I laid out how prompts are different from keywords, why “synthetic keywords” are really just a temporary band-aid, and how fewer clicks might just challenge the age-old cost-per-click (CPC) revenue model.

This follow-up is about what these changes truly mean for us as advertisers, and why holding onto that keyword-era mindset could cost us our competitive edge.

The Great Rewiring Of Search

The biggest shift since we first got keyword-targeted online advertising is now in full swing. People aren’t searching with those relatively concise keywords anymore, the ones we optimized for how Google used to weigh certain words in a query.

Large language models (LLMs) have pretty much removed the shackles from the search bar. Now, users can fire off prompts with hundreds of words, and add even more context.

Think about the 400,000 token context window of GPT-5, which is like tens of thousands of words. Thankfully, most people don’t need that much space to explain what they want, but they are speaking in full sentences now, stutters and all.

Google’s internal ads in AI Mode document shares that early testers of AI Mode are asking queries that are two to three times as long as traditional searches on Google.

And thanks to LLMs’ multi-modal capabilities, users are searching with images (Google reports 20 billion Lens searches per month), drawing sketches, and even sending video. They’re finding what they need in entirely new ways.

Increasingly, users aren’t just looking for a list of what might be relevant. They expect a guided answer from the AI, one that summarizes options based on their personal preferences. People are asking AI to help them decide, not just to find.

And that fundamental change in user behavior is now reshaping the very platforms where these searches happen, starting with Google.

The Impact On Google As The Main Ads Platform

All of this definitely poses a threat to Google’s primary revenue stream. But as I mentioned in a LinkedIn post, the traffic didn’t vanish; it just moved.

Users didn’t ditch Google; they simply stopped using it the way they did when keywords were king. Plus, we’re seeing new players emerge, and search itself has fragmented:

This creates a fresh challenge for us advertisers: How do we design campaigns that actually perform when intent originates in these wildly new ways?

What Q2 Earnings Reports Told Us About AI In Search

The Q2 earnings calls were packed with GenAI details. Some of the most jaw-dropping figures involved the expected infrastructure investments.

Microsoft announced plans to spend an eye-watering $30 billion on capital expenditures in the coming quarter, and Alphabet estimated an $85 billion budget for the next year. I guess we’ll all be clicking a lot of ads to help pay for that. So, where will those ads come from when keywords are slowly being replaced by prompts?

Google shared some numbers to illustrate the scale of this shift. AI Overviews already reach 2 billion users a month. AI Mode itself is up to 100 million. The real question is, how is AI actually enabling better ads, and thus improving monetization?

Google reports:

  • Over 90 Performance Max improvements in the past year drove 10%+ more conversions and value.
  • Google’s AI Max for Search campaigns show a 27% lift in conversions or value over exact or phrase matches.

Microsoft Ads tells a similar story. In Q2 2025, it reported:

  • $13 billion in AI-related ad revenue.
  • Copilot-powered ads drove 2.3 times more conversions than traditional formats.
  • Users were 53% more likely to convert within 30 minutes.

So, what’s an advertiser to do with all this?

What Advertisers Should Do

As shared recently in a conversation with Kasim Aslam, these ecosystems are becoming intent originators. That old “search bar” is now a conversation, a screenshot, or even a voice command.

If your campaigns are still relying on waiting for someone to type a query, you’re showing up to the party late. Smart advertisers don’t just respond to intent; they predict it and position for it.

But how? Well, take a look at the Google products that are driving results for advertisers: They’re the newest AI-first offerings. Performance Max, for example, is keywordless advertising driven by feeds, creative, and audiences.

Another vital step for adapting to this shift is AI Max, which I’d call the most unrestrictive form of keyword advertising.

It blends elements of Dynamic Search Ads (DSAs), automatically created assets, and super broad keywords. This allows your ads to show up no matter how people search, even if they’re using those sprawling, multi-part prompts.

Sure, advertisers can still use today’s best practices, like reviewing search term reports and automatically created assets, then adding negatives or exclusions for the irrelevant ones. But let’s be honest, that’s a short-term, old-model approach.

As AI gains memory and contextual understanding, ads will be shown based on scenarios and user intent that isn’t even explicitly expressed.

Relying solely on negatives won’t cut it. The future demands that advertisers focus on getting involved earlier in the decision-making process and making sure the AI has all the right information to advocate for their brand.

Keywords Aren’t The Lever They Once Were

In the AI Mode era, prompts aren’t just simple queries; they’re rich, multi-turn conversations packed with context.

As I outlined in my last article, these interactions can pull in past sessions, images, and deeply personal preferences. No keyword list in the world can capture that level of nuance.

Tinuiti’s Q2 benchmark report shows Performance Max accounts for 59% of Shopping ad spend and delivers 18% higher click-through rates. This is a clear illustration that the platform is taking control of targeting.

And when structured feeds plus dynamic creative drive a 27% lift in conversions according to Google data, it’s because the creative itself is doing the targeting.

Those journeys happen out of sight, which is the biggest threat to advertisers whose strategies aren’t evolving.

The Real Danger: Invisible Decisions

One of my key takeaways from the AI Mode discussion was the risk of “zero-click” journeys. If the assistant delivers what a user needs inside the conversation, your brand might never get a visit.

According to Adobe Analytics, AI-powered referrals to U.S. retail sites grew 1,200% between July 2024 and February 2025. Traffic from these sources now doubles every 60 days.

These users:

  • Visit 12% more pages per session.
  • Bounce 23% less often.
  • Spend 45% more time browsing (especially in travel and finance verticals).

Even more importantly, 53% of users say they plan to rely on AI tools for shopping going forward.

In short, users are starting their journeys before they reach a traditional search engine, and they’re more engaged when they do. And winning in this environment means rethinking our levers for influence.

Why This Is An Opportunity, Not A Death Sentence

As I argued before, platforms aren’t killing keyword advertising; they’re evolving it. The advertisers winning now are leaning into the new levers:

Signals Over Keywords

  • Use customer relationship management (CRM) data to build high-intent audience lists.
  • Layer first-party data into automated campaign types through conversion value adjustments, audiences, or budget settings.
  • Optimize your product feed with rich attributes so AI has more to work with and knows exactly which products to recommend.
  • Ensure feed hygiene so LLMs have the most current data about your offers.
  • Enhance your website with more data for the LLMs to work with, like data tables, and schema.

Creative As Targeting

  • Build modular ad assets that AI can assemble dynamically: multiple headlines, descriptions, and images tailored to different audiences.
  • Test variations that align with different stages of the buying journey so you’re likely to show in more contextual scenarios across the entire consumer journey, not only at the end.

Measurement Beyond Clicks

  • Frequently evaluate the new metrics in Google Ads for AI Max and Performance Max. Changes are rolling out frequently, enabling smarter optimizations.
  • Track feed impression share by enabling these extra columns in Google Ads.
  • Monitor how often your products are surfaced in AI-driven recommendations, as with the recently updated AI Max report for “search terms and landing pages from AI Max.”
  • Focus your measurement on how well users are able to complete tasks, not just clicks.

The future isn’t about bidding on a query. It’s about supplying the AI with the best “raw ingredients” so you win the recommendation at the exact moment of decision.

That mindset shift is the real competitive advantage in the AI-first era.

The Bottom Line

My previous AI Mode post was about the mechanics of the shift. This one is about the mindset change required to survive it.

Keywords aren’t vanishing, but their role is shrinking fast. In an AI-driven, context-first search landscape, the brands that thrive will stop obsessing over what the user types and start shaping what the AI recommends.

If you can win that moment, you won’t just get found. You’ll get chosen.

More Resources:

Featured Image: Smile Studio AP/Shutterstock

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Sep 9 2025
Google Gemini Adds Audio File Uploads After Being Top User Request via @sejournal, @MattGSouthern

Google’s Gemini app now accepts audio file uploads, answering what the company acknowledges was its most requested feature.

For marketers and content teams, it means you can push recordings straight into Gemini for analysis, summaries, and repurposed content without jumping between tools.

Josh Woodward, VP at Google Labs and Gemini, announced the change on X:

“You can now upload any file to @GeminiApp. Including the #1 request: audio files are now supported!”

What’s New

Gemini can now ingest audio files in the same multi-file workflow you already use for documents and images.

You can attach up to 10 files per prompt, and files inside ZIP archives are supported, which helps when you want to upload raw tracks or several interview takes together.

Limits

  • Free plan: total audio length up to 10 minutes per prompt; up to 5 prompts per day.
  • AI Pro and AI Ultra: total audio length up to 3 hours per prompt.
  • Per prompt: up to 10 files across supported formats. Details are listed in Google’s Help Center.

Why This Matters

If your team works with podcasts, webinars, interviews, or customer calls, this closes a gap that often forced a separate transcription step.

You can upload a full interview and turn it into show notes, pull quotes, or a working draft in one place. It also helps meeting-heavy teams: a recorded strategy session can become action items and a brief without exporting to another tool first.

For agencies and networks, batching multiple episodes or takes into one prompt reduces friction in weekly workflows.

The practical win is fewer handoffs: source audio goes in, and the outlines, summaries, and excerpts you need come out. Inside the same system you already use for text prompting.

Quick Tip

Upload your audio together with any supporting context in the same prompt. That gives Gemini the grounding it needs to produce cleaner summaries and more accurate excerpts.

If you’re testing on the free tier, plan around the 10-minute ceiling; longer content is best on AI Pro or Ultra.

Looking Ahead

Google’s limits pages do change, so keep an eye on total length, file-count rules, and any new guardrails that affect longer recordings or larger teams. Also watch for deeper Workspace tie-ins (for example, easier handoffs from Meet recordings) that would streamline getting audio into Gemini without manual uploads.

Featured Image: Photo Agency/Shutterstock

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Sep 8 2025
Anthropic Agrees To $1.5B Settlement Over Pirated Books via @sejournal, @MattGSouthern

Anthropic agreed to a proposed $1.5 billion settlement in Bartz v. Anthropic over claims it downloaded pirated books to help train Claude.

If approved, plaintiffs’ counsel says it would be the largest U.S. copyright recovery to date. A preliminary approval hearing is set for today.

In June, Judge William Alsup held that training on lawfully obtained books can qualify as fair use, while copying and storing millions of pirated books is infringement. That order set the stage for settlement talks.

Settlement Details

The deal would pay about $3,000 per eligible title, with an estimated class size of roughly 500,000 books. Plaintiffs allege Anthropic pulled at least 7 million copies from piracy sites Library Genesis and Pirate Library Mirror.

Justin Nelson, counsel for the authors, said:

“As best as we can tell, it’s the largest copyright recovery ever.”

How Payouts Would Work

According to the Authors Guild’s summary, the fund is paid in four tranches after court approvals: $300M soon after preliminary approval, $300M after final approval, then $450M at 12 months and 450M at 24 months, with interest accruing in escrow.

A final “Works List” is due October 10, which will drive a searchable database for claimants.

The Guild notes the agreement requires destruction of pirated copies and resolves only past conduct.

Why This Matters

If you rely on AI tools in content workflows, provenance now matters more. Expect more licensing deals and clearer disclosures from vendors about training data sources.

For publishers and creators, the per-work payout sets a reference point that may strengthen negotiating leverage in future licensing talks.

Looking Ahead

The judge will consider preliminary approval today. If granted, the notice process begins this fall and payments to rightsholders would follow final approval and claims processing, funded on the installment schedule above.

Featured Image: Tigarto/Shutterstock

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