If it looks like an AI hallucination problem, and sounds like an AI hallucination problem, it’s probably a data hygiene problem.
I’ve sat through dozens of demos this year where marketing leaders show me their shiny new AI agent, ask it a basic question, and watch it confidently spit out information that’s either outdated, conflicting, or flat-out wrong.
The immediate reaction is to blame the AI: “Oh, sorry the AI hallucinated. Let’s try something different.”
Don’t shoot the messenger, as the saying goes. While the AI is the messenger bringing you what looks like inaccurate data or hallucination, it’s really sending a deeper message: Your data is a mess.
The AI is simply reflecting that mess back to you at scale.
Almost half of the data feeding your AI systems, your reporting dashboards, and your strategic decisions is wrong. And we wonder why AI agents give vague answers, contradict themselves, or pull messaging that no one’s used since 2022.
Here’s what I see in nearly every enterprise:
Three teams operating with three different definitions of ideal customer profile (ICP).
Marketing defines “conversion” one way, sales defines it another.
Buyer data scattered across six systems that barely acknowledge each other’s existence.
A battlecard last updated in 2019 still floating around, treated like gospel by your AI agent.
When your foundational data argues with itself, AI doesn’t know which version to believe. So it picks one. Sometimes correctly. Often not.
Why Clean Data Matters More Than Smart AI
AI isn’t magic. It reflects whatever you feed it: the good, the bad, and the three-years-outdated.
Everyone wants the “build an agent” sexy moment. The product demo that has everyone applauding. The efficiency gains that guarantee a great review, heck, maybe even a raise.
But the thing that makes AI useful is the boring, unsexy, foundational work of data discipline.
I’ve watched companies spend six figures on AI infrastructure while their product catalog still has duplicate entries from a 2021 migration. I’ve seen sales teams adopt AI coaching tools while their CRM defines “qualified lead” three different ways depending on which region you ask.
The AI works exactly as designed. The problem is what it’s designed to work with.
If your system is messy, AI can’t clean it up (at least, not yet). It amplifies the mess at scale, across every interaction. As much as we would like for it to, even the sexiest AI model in the world won’t save you if your data foundation is broken.
The Real Cost Of Bad Data Hygiene
When your data is inaccurate, inconsistent, or outdated, mistakes are inevitable. These can get risky quickly, especially if they negatively impact customer experience or revenue.
Here’s what that looks like in practice:
Your sales agent gives prospects pricing that changed six months ago because nobody updated the product sheet it’s trained on.
Your content generation tool pulls brand messaging from 2020 because the 2026 messaging framework lives in a deck on someone’s desktop.
Your lead scoring AI uses ICP criteria that marketing and sales never agreed on, so you’re nurturing the wrong prospects while ignoring the right ones.
Your sales enablement agent recommends a case study for a product you discontinued last quarter because nobody archived the old collateral.
This is happening every single week in enterprises that have invested millions in AI transformation. And most teams don’t even realize it until a customer or prospect points it out.
Where To Start: 5 Steps To Fix Your Data Foundation
The good news: You don’t need a massive transformation initiative to fix this. You need discipline and ownership.
1. Audit What Your AI Can Actually See
Before you can fix your data problem, you need to understand its scope.
Pull every document, spreadsheet, presentation, and database your AI systems have access to. Don’t assume. Actually look.
You’ll more than likely find:
Conflicting ICP definitions across departments.
Outdated pricing from previous years.
Messaging from three rebrand cycles ago.
Competitive intel that no longer reflects market reality.
Case studies for products you no longer sell.
Retire what’s wrong. Update what’s salvageable. Be ruthless about what stays and what goes.
2. Create One Source Of Truth
This is non-negotiable. Pick one system for every definition that matters to your business:
ICP criteria.
Conversion stage definitions.
Territory assignments.
Product positioning.
Competitive differentiators.
Everyone pulls from it. No exceptions. No “but our team does it differently.”
When marketing and sales use different definitions, your AI can’t arbitrate. It picks one randomly. Sometimes it picks both and contradicts itself across interactions.
One source of truth eliminates that chaos.
3. Set Expiration Dates For Everything
Every asset your AI can access should have a “valid until” date.
Battlecards. Case studies. Competitive intelligence. Messaging frameworks. Product specs.
When it expires, it automatically disappears from AI access. No manual cleanup required. No hoping someone remembers to archive old content.
Stale data is worse than no data. At least with no data, your AI admits it doesn’t know. With stale data, it confidently delivers wrong information.
4. Test What Your AI Actually Knows
Don’t assume your AI is working correctly. Test it.
Ask basic questions:
“What’s our ICP?”
“How do we define a qualified lead?”
“What’s our current pricing for [product]?”
“What differentiates us from [competitor]?”
If the answers conflict with what you know is true, you just found your data hygiene problem.
Run these tests monthly. Your business changes. Your data should change with it.
5. Assign Someone To Own It
Data discipline without ownership is a Slack thread that goes nowhere.
One person needs to be explicitly responsible for maintaining your source of truth. Not as an “additional responsibility.” As a core part of their role.
This person:
Reviews and approves all updates to the source of truth.
Sets and enforces expiration dates for assets.
Runs monthly audits of what AI can access.
Coordinates with teams to retire outdated content.
Reports on data quality metrics.
Without ownership, your data hygiene initiative dies in three months when everyone gets busy with other priorities.
The Bottom Line: Foundation Before Flash
If you don’t fix the mess, AI will scale the mess.
Deploying powerful AI on top of chaotic data is at best inefficient, but at worst, it can actively damage your brand, your customer relationships, and your competitive position.
You can have the most sophisticated AI model in the world. The best prompts. The most expensive infrastructure. None of it matters if you’re feeding it garbage. It takes a disciplined foundation to make it work.
It’s like seeing someone with perfectly white teeth and thinking they just got lucky. What you don’t see is the daily flossing, the regular dental cleanings, the discipline of avoiding sugar and brushing twice a day for years.
Or watching an Olympic athlete make a performance look effortless. You’re not seeing the 5 a.m. training sessions, the strict diet, the thousands of hours of practice that nobody applauds.
The same applies to AI.
To get real value and ROI from AI, start with setting it up for success with the right data foundation. Yes, it might not be the most glamorous or exciting work. But it is what makes the glamorous and exciting possible.
Remember, your AI isn’t hallucinating. It’s telling you exactly what your data looks like.
WooCommerce announced that it will roll out integration with Stripe’s Agentic Commerce Suite, which will enable AI shopping assistants to conduct transactions.
Agentic AI Shopping
Agentic AI seems a long way off but OpenAI currently supports end-to-end shopping from the discovery and comparison stages to completing purchases. With the rollout in WooCommerce the infrastructure will be in place to enable over four million stores to be accept product browsing and payments through AI agents.
Stripe Agentic Commerce Suite
Stripe’s Agentic Commerce Suite uses the Agentic Commerce Protocol (ACP), an open source protocol jointly created by Stripe and OpenAI. ACP is model agnostic and does not lock in users to any particular payment provider.
ACP is compatible with the Model Context Protocol (MCP) which was created by Anthropic initially for connecting AI models to external data. The significance is that MCP enables models to call APIs, retrieve data, and perform actions.
“WooCommerce is proud to be a launch partner. Woo merchants will be among the first to benefit when Agentic Commerce Suite rolls out in the coming months.
This is a significant moment for WooCommerce merchants. Instead of building custom integrations for every new AI shopping assistant or platform, you’ll be able to connect your product catalog once and reach customers shopping through whichever AI agent they prefer. Stripe handles discovery, checkout, payments, and fraud protection, while you continue using your existing WooCommerce + Stripe stack.”
This represents a step toward putting the necessary infrastructure in place to enable consumers to interact with AI as part of a new shopping experience. The very near future may see a dramatic change in shopping habits, something SEOs and merchants will have to consider.
Google has updated Search Live with Gemini 2.5 Flash Native Audio, upgrading how voice functions inside Search while also extending the model’s use across translation and live voice agents. The update introduces more natural spoken responses in Search Live and reflects Google’s effort to improve natural voice queries, treating voice as a core interface as a way for users to get everything they can get from regular search plus enabling them to ask questions about the physical world around them and receive immediate voice translations between two people speaking different languages.
The new updated voice capabilities, rolling out this week in the United States, will enable Google’s voice responses to sound more natural and can even be slowed down for instructional content.
According to Google:
“When you go Live with Search, you can have a back-and-forth voice conversation in AI Mode to get real-time help and quickly find relevant sites across the web. And now, thanks to our latest Gemini model for native audio, the responses on Search Live will be more fluid and expressive than ever before.”
Broader Gemini Native Audio Rollout
This Search upgrade is part of a broader update to Gemini 2.5 Flash Native Audio rolling out across Google’s ecosystem, including Gemini Live (in the Gemini App), Google AI Studio, and Vertex AI. The model processes spoken audio in real time and produces fluid spoken responses, reducing barriers to natural conversation, reducing friction in live interactions. Although Google’s announcement didn’t say that the model was a speech-to-speech model (as opposed to speech-to-text then text-to-speech), this update follows Google’s October announcement of “Speech-to-Retrieval (S2R). It’s a neural network-based machine-learning model trained on large datasets of paired audio queries.”
These changes show Google treating native audio as a core capability across consumer-facing products, making it easier for users to ask and receive information about the physical world around them in a natural manner that wasn’t previously possible.
Improvements For Voice-Based Systems
For developers and enterprises building voice-based systems, Google says the updated model improves reliability in several areas. Gemini 2.5 Flash Native Audio more consistently triggers external functions during conversations, follows complex instructions, and maintains context across multiple turns. These improvements make live voice agents more dependable in real-world workflows, where misinterpreted instructions or broken conversational flow reduce usability.
Smooth Conversational Translation
Beyond Search and voice agents, the update introduces native support for “live speech-to-speech translation.” Gemini translates spoken language in real time, either by continuously translating ambient speech into a target language or by handling conversations between speakers of different languages in both directions. The system preserves vocal characteristics such as speech rhythm and emphasis, supporting translation that sounds smoother and conversational.
Google highlights several capabilities supporting this translation feature, including broad language coverage, automatic language detection, multilingual input handling, and noise filtering for everyday environments. These features reduce setup friction and allow translation to occur passively during conversation rather than through manual controls. The result is a translation experience that behaves much like an actual person in the middle translating between two people.
Voice Search Realizing Google’s Aspirations
The update reflects Google’s continued iteration of voice search toward an ideal that was originally inspired by the science fiction voice interactions between humans and computers in the popular Star Trek television and movie series.
How people use Microsoft Copilot depends on whether they’re at a desk or on their phone.
That is the core theme in the company’s analysis of 37.5 million Copilot conversations sampled between January and September.
The research examines consumer Copilot usage patterns across device types and time of day. The authors say they used machine-based classifiers to categorize conversations by topic and intent without any human review of the messages.
What The Report Says
On mobile, Health and Fitness is the most common topic throughout the day
The authors summarize the split this way:
“On mobile, health is the dominant topic, which is consistent across every hour and every month we observed, with users seeking not just information but also advice.”
Desktop usage follows a different rhythm. Technology leads as the top topic overall, but the researchers report that work-related conversations rise during business hours.
They describe “three distinct modes of interaction: the workday, the constant personal companion, and the introspective night.”
During the workday, the paper says:
Between 8 a.m. and 5 p.m., “Work and Career” overtakes “Technology” as the top topic on desktop.
Education and science topics rise during business hours compared to nighttime.
Outside business hours, the paper describes a shift toward more personal and reflective topics. For example, it reports that “Religion and Philosophy” rises in rank during late-night hours through dawn.
Programming conversations are more common on weekdays, while gaming rises on weekends. They also note a spike in relationship conversations on Valentine’s Day.
Methodology Caveats
A few limitations are worth keeping in mind.
This is a preprint, so it hasn’t been peer reviewed. It also focuses on consumer Copilot usage and excludes enterprise-authenticated traffic, so it doesn’t describe how Copilot is used inside Microsoft 365 at work.
Finally, the topic and intent labels come from automated classifiers, which means the results reflect how Microsoft’s system groups conversations, not a human-coded review.
Why This Matters
This paper suggests that the use of AI chatbots varies with context. The researchers describe mobile behavior as consistently health-oriented, while desktop behavior is more tied to the workday.
The researchers connect the mobile health pattern to how people use their phones. They write:
“This suggests a device-specific usage pattern where the phone serves as a constant confidant for physical well-being, regardless of the user’s schedule.”
The big takeaway is that “Copilot usage” is not one uniform behavior. Device and time of day appear to shape what people ask for, and how they ask it.
Looking Ahead
Enterprise usage patterns may look different, especially inside Microsoft 365. Any follow-up research that includes workplace contexts, or that validates these patterns outside Microsoft’s own tooling and taxonomy, would help clarify how broadly these findings apply.
I’m carefully watching the development of agentic SEO, as I believe over the next few years, as capabilities improve, agents will have a significant impact on the industry. I’m not suggesting this will be a seamless replacement of talent with a highly capable machine intelligence. There is going to be a lot of trial and error, but I do think we are going to see radical shifts in how the online space operates. Not unlike how automation transformed manufacturing.
Marie Haynes has long been a well-known expert in the industry who shared her learnings on E-E-A-T and Google’s algorithm through her popular Search News You Can Use newsletter.
A few years ago, Marie made the decision to retire her SEO agency and went all in on learning AI systems, as she believes we’re at the beginning of a profound transformation.
Marie wrote a recent article, “Hype or not, should you be investing in AI agents?” about what SEOs need to understand about this rapidly developing space. So, I invited her to IMHO to dive more into this topic.
Marie believes AI will radically change our world for the better, and she believes every business will have AI agents.
You can watch the full interview with Marie on the IMHO recording at the end, or continue reading the article summary.
“The idea that we optimize for appearing as one of the 10 blue links on Google is already gone.”
Experimenting With Gemini Gems
Marie’s practical advice for anyone wanting to understand agents is to start with Gems:
“If you take one thing from this conversation, it’s to try to create some Gemini Gems,” Marie emphasized. “Eventually I’m fairly certain that these gems will morph into agentic workflows.”
To illustrate, she shared a process she called her “originality Gem,” which contains a 500+ word prompt that captures how she evaluates content, along with examples of truly original content in its knowledge base.
“We’re not far from the day where all of my processes that I do for SEO can be handled by agentic workflows that occasionally pull on me for some advice,” Marie said.
The Power Of Chaining Agents
The next progression and real potential come from chaining agents together to create agentic workflows.
The power that this gives opportunity to is that we can use our knowledge and experience to teach AI like a team of assistants to do the work that can be automated.
We would then orchestrate the process and, like a conductor, sit and guide the agents to perform the work as we become the human-in-the-loop to review the output.
Once we have downloaded our knowledge to the agents, and the systems work, we can scale ourselves to handle exponential clients.
“Instead of me handling just a small handful of clients, all of a sudden I could have a hundred clients and do the same work because it’s all going through my workflow,” Marie said.
The challenge here is the skill in prompting the agents and constructing them to achieve the desired output.
“The future of our industry is not about optimizing for an engine, but about acting as the interface between businesses and technology, and we will be the human experts who teach, guide, and implement AI agents.”
Why Gemini Over ChatGPT
I asked Marie why she focuses on Gemini over ChatGPT, and her response was based on futureproofing: “The main reason why I use Gemini is not to accomplish things today, but to grow my skills in what’s coming tomorrow.”
Marie went on to explain that “Google’s got a whole ecosystem that you can see it coming together like right now,” and she believes that Google will be the winner in the AI race.
“I think that Google is going to win the game. I think it’s always been their game to win. So I make it a point to use Gemini as much as I can.”
Transformations Will Follow The Money
Marie’s prediction for the next few years is for workflows to become embedded. “Sundar Pichai, CEO of Google, said this way back in March, that, in two to four years, every agentic workflow will be deeply embedded into our day-to-day work.”
However, she thinks the real transformations will come when businesses start making money from agentic workflows.
“It’s wild how many trillions of dollars are being spent on developing AI, yet there’s not a whole lot of financial output at this point,” Marie noted, referencing a McKinsey study showing 95% of businesses using AI aren’t making money from it yet [Editor’s note: McKinsey was 80%; MIT said 95%].
“It’s very similar to SEO. There was a day where there were just a small handful of people who figured out how to improve on Google. Once people started making good money from understanding SEO, there was a lot of attention. Tools were created and a whole industry popped up. I think that’s going to happen again. Will it be within the next 12 months? I don’t know. I feel like it might be a little bit longer.”
What SEOs Should Do Now
Overwhelm is a real issue to be aware of, and with developments moving so quickly, there is a huge learning curve to essentially retrain. Even for those working on this full-time.
Marie made a commitment when she went all in on AI research. “I made it my full-time job to stay on top of what’s happening, and even I get overwhelmed with all the stuff that’s happening with AI,” she explained.
“The next time you go to do a task, try to create an agent that would do this for you,” she suggested. Even if you don’t finish, you’ll learn skills for the next attempt.
Also, persevere instead of taking the first failure. “Try to figure out what they can do, instead of just telling everybody, ‘Oh, it can’t do this.’ Find ways you can use it.”
For development teams, she recommends vibe coding with tools like Google’s Anti Gravity or AI Studio. “You can deploy a whole website without even knowing any HTML,” Marie said.
She also advocates for deep research reports using either Gemini or ChatGPT to analyze how competitors are using AI, providing immediate value to clients while building skills.
The Future Of SEO
Marie referenced Sundar Pichai calling AI technology more profound than fire or electricity in its impact on society. Despite acknowledging her bias after investing significant time in understanding AI, she maintains there’s going to be societal disruption.
“Being able to understand what’s happening in the world and distill it down to what’s important to your clients will be a superpower,” she said. Although, she does admit, there is still a lot of learning and grey areas to move through as we navigate the edge of technology.
“If you’re feeling lost, you’re not alone because imagine right now we’re sort of at the forefront of all of these changes happening.”
For those who do persevere, there will be significant rewards. Eventually, business owners will be clamoring for people who can explain AI and implement it. The professionals who develop these skills now will be extremely valuable in the future.
“The people who know how to use AI, know how to create agents, and know how to make money from AI are going to be extremely valuable in the future.”
Watch the full video interview with Marie Haynes here:
Thank you to Marie Haynes for offering her insights and being my guest on IMHO.
More Resources:
Featured Image: Shelley Walsh/Search Engine Journal
This post was sponsored by Editorial.Link. The opinions expressed in this article are the sponsor’s own.
“How do you find link-building services? You don’t, they find you,” goes the industry joke. It’s enough to think about backlinks and dozens of pitches that hit your inbox.
However, most of them offer spammy links with little long-term value. Link farms, PBNs, the lot.
This type of saturated market makes it hard to find a reputable link building agency that can navigate the current AI-influenced search landscape.
That’s why we’ve put together this guide.
We’ll share a set of steps that will help you vet link providers so you can find a reliable partner that will set you up for success in organic and AI search.
1. Understand How AI-Driven Search Changes Link Building
Before you can vet an agency, you must understand how the “AI-influenced” landscape is different. Many agencies are still stuck in the old playbook, which includes chasing guest posts, Domain Rating (DR), and raw link volume.
When vetting a service for AI-driven search, your criteria must shift from “How many links can you get?” to “Can you build discoverable authority that earns citations?”
This means looking for agencies that build your niche authority through tactics like original data studies, digital PR, and expert quotes, not just paid posts.
2. Verify Their Expertise and AI-Search Readiness
The first test is simple: do they practice what they preach?
Check Their Own AI & Search Visibility
Check the agency’s rankings in organic and AI search for major keywords in their sector.
Let’s say you want to vet Editorial.Link. If you search for “best link building services,” you will find it is one of the link providers listed in the AI Overviews.
Screenshot of Google’s AI Overviews, November 2025
It doesn’t mean an agency isn’t worth your time just because it doesn’t rank high, as some services thrive on referrals and don’t focus on their own SEO.
However, if they do rank, that’s a major green flag. SEO is a highly competitive niche; ranking their own website demonstrates the expertise to deliver similar results for you.
Ensure Their Tactics Build Citation-Worthy Authority
A modern agency’s strategy should focus on earning citations.
Ask them these questions to see whether they’ve adapted:
Do they talk about AI visibility, citation tracking, or brand mentions?
Do they build links through original data studies, digital PR, and expert quotes?
Can they show examples of clients featured in AI Overviews, Chat GPT, or Perplexity answers?
Can they help you get a link from top listicles in your niche? Ahrefs’ data shows “Best X” list posts dominated the field. They made up 43,8% of all pages referenced in the responses, and the gap between them and every other format looked huge. You can find relevant listicles in your niche using free services, like listicle.com.
Screenshot of Listicle, November 2025
3. Scrutinize Their Track Record Via Reviews, Case Studies & Link Samples
Past performance is a strong indicator of future results.
Analyze Third-Party Reviews
Reviews on independent platforms like Clutch, Trustpilot, or G2 reveal genuine clients’ sentiment better than hand-picked testimonials on a website.
When studying reviews, look for:
Mentions of real campaigns or outcomes.
Verified client names or company profiles.
Recent activity, such as new reviews, shows a steady flow of new business.
The total number of reviews (the more, the more representative).
Patterns in negative reviews and how the agency responds to them.
Screenshot of Editorial.Link’s profile on Clutch, November 2025
Dig Into Their Case Studies
Case studies and customer stories offer proof of concept and provide insights into their processes, strategies, and industry fit.
While case studies with named clients are ideal, some top-tier agencies are bound by client NDAs for competitive reasons. Be wary if all their examples are anonymous and vague, but don’t dismiss a vendor just for protecting client confidentiality.
If the clients’ names are provided, don’t take any figures at face value.
Use an SEO tool to examine their link profiles. If you know the campaign’s timeframe, zero in on that period to see how many links they acquired, their quality, and their relevance.
Screenshot of Thrive Internet Marketing, November 2025
Audit Their Link Quality
Inspecting link quality is the ultimate litmus test.
An agency’s theoretical strategy doesn’t matter if its final product is spam. Ask for 3 – 5 examples of links they have built for recent clients.
Once you have the samples, don’t just look at the linking site’s DR. Audit them with this checklist:
Editorial relevance: Is the linking page topically relevant to the target page?
Site authority & traffic: Does the linking website have real, organic traffic?
Placement & context: Is the link placed editorially within the body of an article?
AI-citation worthiness: Is this an authoritative site Google AI Overview, ChatGPT, or Perplexity would cite (e.g., a reputable industry publication or a data-driven report)?
4. Evaluate Their Process, Pricing & Guarantees
A reliable link-building service is fully transparent about its process and what you’re paying for.
Look For A Transparent Process
Can you see what you’re paying for? A reliable service will outline its process or share a list of potential prospects before starting outreach.
Ask them for a sample report. Does it include anchor texts, website GEO, URLs, target pages, and publication dates? A vague “built 20 links” report doesn’t cut it.
Finally, check if they offer consulting services.
For example, can they help you choose target pages that will benefit from a link boost most?
Or are they just a link-placing service, as this signals a lack of expertise?
Analyze Their Pricing Model
Price is a direct indicator of quality.
When someone offers links for $100 – $200 a pop, they are typically from PBNs or bulk guest posts, and frequently disappear within months.
Valuable backlinks from trusted sites cost significantly more on average, $508.95, according to the Editorial.Link report.
Prospecting, outreach, content creation, and communication require substantial time and effort.
Reputable agencies work on one of two models:
Retainer model: A fixed monthly fee for a consistent flow of links.
Custom outreach: Tailored campaigns with flexible volume and pricing.
Scrutinize Their “Guarantees” For Red Flags
This is where unrealistic promises expose low-quality vendors.
A reputable digital PR agency, for example, won’t guarantee the number of earned links. The final result depends on how well a story resonates with journalists.
The same applies to “guaranteed DR or DA.” These metrics don’t directly affect rankings, and it’s impossible to guarantee which websites will pick up a story.
Choosing A Link Building Partner For The AI Search Era
Not all link-building services have the necessary expertise to help you build visibility in the age of AI search.
When choosing your link-building partner, look for a proven track record, transparency, and adaptability.
A service with a strong search presence, demonstrable results, and a focus on AI visibility is a safer bet than one making unsubstantiated claims.
The European Commission has launched an antitrust inquiry into Google to determine whether the company has violated EU competition rules, partly focusing on whether Google has used creator and publisher content in ways that leave publishers unable to refuse such use without risking their search traffic. It is also looking into whether Google is granting itself privileged access to YouTube content for AI in a way that leaves competitors at a disadvantage.
How Google’s Terms May Pressure Publishers and Creators
The Commission is focusing on publisher content is used by AI Overviews and AI Mode to generate answers but without a way to compensate the publishers or for them to opt out of having their content used to generate summaries.
They write:
“The Commission will investigate to what extent the generation of AI Overviews and AI Mode by Google is based on web publishers’ content without appropriate compensation for that, and without the possibility for publishers to refuse without losing access to Google Search. Indeed, many publishers depend on Google Search for user traffic, and they do not want to risk losing access to it.”
This raises concerns that Google may be using publisher content in its AI products without offering a workable opt-out, leaving publishers who rely on Search traffic with little choice but to accept this use.
Use of YouTube Content to Train Google’s AI Models
The Commission is also examining Google’s use of YouTube videos and other creator content for training its generative AI models. According to the announcement, creators “have an obligation to grant Google permission to use their data for different purposes, including for training generative AI models,” and cannot upload content while withholding that permission. Google provides no payment for this use while blocking rival AI developers from training on YouTube content under YouTube’s policies.
This mix of mandatory access for Google, limits on competitors, and no payment for creators underpins the Commission’s concern that Google may be giving itself preferred access to YouTube content in a way that may harm the wider AI market.
The Commission has notified Google that it has opened an investigation into whether they have breached EU competition rules prohibiting the abuse of a dominant position.
Google is publicly pushing back on an Adweek report that claimed the company told advertising clients it plans to bring ads to its Gemini AI chatbot next year.
Dan Taylor, Google’s Vice President of Global Ads, responded directly on X shortly after the story published, calling the report inaccurate and denying any plans to monetize the Gemini app.
The Original Report
Adweek’s Trishla Ostwal reported that Google had informed advertising clients about plans to introduce ads to Gemini. According to the exclusive story, Google representatives held calls with at least two advertising clients indicating that ad placements in Gemini were targeted for a 2026 rollout.
The agency buyers who spoke to Adweek remained anonymous. They said details on ad formats, pricing, and testing were unclear, and that Google had not shared prototypes or technical specifications about how ads would appear in the chatbot.
Notably, the report said this plan would be separate from advertisements in AI Mode, Google’s AI-powered search experience.
Google’s Response
Taylor disputed the claims publicly on X, writing: “This story is based on uninformed, anonymous sources who are making inaccurate claims. There are no ads in the Gemini app and there are no current plans to change that.”
Google’s official AdsLiaison account amplified the denial, reiterating that there are no ads in the Gemini app and no current plans to add them, and pointing out that ads currently appear in AI Overviews in English in the US, with expansion to more English-speaking countries, and are being tested in AI Mode.
Logan Kilpatrick, who works on Google’s Gemini team, responded to Taylor’s post with “thanks for clarifying!!”
Where Google Is Monetizing AI
While the Gemini app itself remains ad-free according to Google, the company is actively monetizing other AI-powered search experiences.
Google began showing ads in AI Overviews earlier this year and has been expanding that program to additional English-speaking countries. The company also continues testing advertisements within AI Mode.
Why This Matters
The question of how AI chatbots will be monetized has become increasingly relevant as these products gain mainstream adoption. Google, OpenAI, and other AI companies face pressure to generate revenue from expensive-to-run conversational AI products.
Just last week, code discovered in ChatGPT’s Android app suggested OpenAI may be building an advertising framework, though the company has not confirmed any plans to introduce ads.
For now, Google maintains that Gemini users won’t see ads in the chatbot app. Whether that position changes as the AI landscape evolves remains to be seen.
For two decades, the arrangement between search engines and publishers was a symbiotic relationship where publishers allowed crawling, and search engines sent referral traffic back. That traffic helped to fund content creation for publishers through ads and subscriptions.
AI features are changing this, and the deal is starting to break down.
AI Overviews, ChatGPT, and answer engines keep users within their platform instead of sending them to source sites. The result is publishers are watching their traffic decline while AI companies crawl more content than ever.
New payment models are emerging to replace the old economics. some involve usage-based revenue sharing, others are flat licensing deals worth millions, and a few have ended in court settlements. But the terms vary widely, and it’s unclear whether any model can sustain the content ecosystem that AI depends on.
This article examines the payment models taking shape, how different publishers are responding, and what SEO professionals should consider as the industry figures out sustainable economics.
The crawl-to-referral ratio shows how unbalanced this is. Cloudflare’s analysis tracks Google Search maintaining roughly a 10:1 ratio, crawling about 10 pages for every referral sent back. OpenAI’s ratio was estimated at around 1,200:1 to 1,700:1.
Fewer pageviews mean fewer ad impressions, lower subscription conversions, and reduced affiliate revenue.
Payment Models Taking Shape
Three payment models are emerging.
1. Usage-Based Revenue Sharing
Perplexity launched its Comet Plus program in 2025. The company shares subscription revenue with publishers after keeping a cut for compute costs, though the exact split isn’t disclosed.
These models tie pay to usage, but the pools stay small compared to traditional search revenue and scaling depends on converting free users to paid subscribers.
These arrangements bundle three rights: training data access using archives to improve models, real-time content display with attribution in ChatGPT, and technology access letting publishers use OpenAI tools.
AI companies need both historical archives and current content, but this creates tiers where publishers with vast archives can negotiate deals while smaller publishers lack leverage.
Anthropic settled with authors for $1.5 billion after Judge William Alsup’s June ruling in Bartz v. Anthropic. The ruling said training on legally purchased books was fair use. Downloading from pirate sites was infringement.
The settlement shows AI companies can afford to pay even while arguing in court they shouldn’t have to, and it provides a public benchmark other negotiations may reference, though specific terms remain sealed.
Publishers accepting deals cite new revenue streams, legal protection from copyright claims, influence over AI development, and recognition that AI search adoption appears inevitable, with many viewing early partnerships as positioning for future leverage.
Publishers Pursuing Litigation
The New York Times sued OpenAI and Microsoft in 2023. The complaint argues the companies created “a multi-billion-dollar for-profit business built in large part on the unlicensed exploitation of copyrighted works.”
Publishers refusing deals say the money’s too low and worry that accepting bad terms now legitimizes them going forward, plus AI summaries directly compete with their work.
Trade Organization Positions
Danielle Coffey, CEO of News/Media Alliance, called Google’s AI Mode practices “parasitic, unsustainable and pose a real existential threat.” She suggests that AI systems are only as good as the content they use to train them.
Jason Kint of Digital Content Next noted that despite Google sending large monthly revenue checks through advertising, 78% of member digital revenue still comes from ads. Every point of search traffic lost “squeezes the budgets that fund investigative reporting.”
Both organizations demand that AI systems provide transparency, clearly attribute content, respect publishers’ roles, comply with competition laws, and not misrepresent original works.
The Emerging Division: Licensed Web Vs. Open Web
The payment model differences are creating two tiers of web content with different economics.
A “Licensed Web” consists of premium content behind APIs and licensing agreements. Publishers with vast archives, specialized expertise, or unique data sets are negotiating direct access deals with LLM companies. This content gets used for training and real-time retrieval with attribution and compensation.
The “Open Web” includes crawlable pages without licensing agreements. User-generated content, marketing material, commodity information, and sites lacking leverage to negotiate terms. This content may still get crawled and used, but without direct compensation beyond minimal referral traffic.
This setup can lead to mismatched incentives. Publishers investing in differentiated, high-quality content may have licensing options to support their work. Meanwhile, those creating more easily replaceable information might struggle with commoditization, making it harder to find clear ways to earn revenue.
For practitioners, focus on developing your own research, unique data sets, specialized expertise, and original reporting. This increases both traditional search value and potential licensing value to AI platforms.
How Payment Models Are Reshaping SEO And Content Strategy
The shift from traffic to licensing is forcing changes across SEO.
The Citation Vs. Click Problem
Traditional SEO centered on rankings that drove clicks, but LLM citations work differently as content appears in AI answers with attribution, but fewer click-throughs. Lily Ray believes SEO is no longer just about ranking and traffic.
Practitioners are now tracking engagement quality, conversion rates, branded search, and direct traffic alongside traditional metrics. Some are quantifying AI citations across ChatGPT, Perplexity, and other platforms. This provides visibility into brand mentions even when referrals don’t materialize.
Bot Access Becomes A Business Decision
Publishers today find themselves making decisions about blocking content via robots.txt. These choices weren’t even considered two years ago. The decision weighs AI visibility with concerns about potential traffic loss and the benefits of licensing.
Many content publishers are open to allowing bot access, valuing their presence in AI results more than guarding content that competitors also produce. News organizations prioritize speed and broad coverage for breaking stories, aiming to reach as many people as possible.
On the other hand, some publishers choose to restrict access to their high-value research and specialized insights, knowing that scarcity can give them stronger negotiating power. Those with paywalled analysis often block AI crawlers to protect their subscription models, ensuring they maintain control over their most valuable content.
ProRata and TollBit offer selective licensing as a middle ground. Publishers maintain AI visibility while getting paid. But AI companies haven’t widely adopted these platforms.
Measurement Systems Under Pressure
Traffic declines may trigger discussions with stakeholders who expect a recovery, and for sites that rely solely on advertising, this can be a challenging discussion to have.
Publishers are exploring alternative revenue models such as subscriptions, memberships, consulting, events, and affiliate partnerships, while also prioritizing email, newsletters, and apps.
Branded search remains more stable than overall traffic levels, emphasizing the importance of brand-building beyond search rankings.
Content Investment Questions
Payment uncertainty can make it hard to decide what content is worth investing in. Publishers with licensing deals might focus on what AI companies need for training or retrieval, while those without deals have to consider different factors.
The division between Licensed Web and Open Web influences these choices. Original research, unique data, and specialized expertise may justify different levels of investment compared to more common material.
Smaller publishers often lack the leverage of licensing. Creating high-quality content while competing with AI-generated summaries that don’t drive traffic raises ongoing questions about sustainability.
Content Sustainability Concerns
Revenue declines are forcing news organizations to cut staff, reducing investigative capacity and the production of original reporting.
The Society of Authors reports 12,000+ members have written letters saying they “do not consent” to AI training. That signals creative professionals reconsidering publication if compensation doesn’t materialize.
More content is moving behind paywalls, which protects revenue but limits free information access. The News/Media Alliance warns that without fair compensation for publisher content, AI practices pose a significant threat to ongoing investment in journalism.
The challenge is that AI companies really rely on publishers to provide high-quality training data. But AI systems that don’t generate traffic can make it harder for publishers to fund their content creation efforts.
Right now, payment models might work well for big publishers who have more power, but mid-sized and small publishers face more uncertain financial situations.
Those with direct relationships to their audience and multiple sources of income are generally in a stronger position compared to those mainly relying on ads.
What’s Likely Next
Current LLM payment models don’t match what publishers earned from search traffic, and they also don’t reflect what AI companies extract through crawling.
Publishers are dividing into distinct camps, with some angling for deals while others are betting litigation will establish better terms than individual negotiations.
Trade organizations are pushing for regulatory solutions, but AI companies maintain their current approach works. OpenAI points to expanding partnerships and says deals provide fair value. Perplexity argues its revenue-sharing model aligns incentives. Google hasn’t announced plans beyond existing traffic-sharing arrangements.
What happens next depends on litigation outcomes, regulatory action, and whether market pressure forces AI platforms to improve terms.
Multiple paths forward remain possible, and for now, publishers face immediate decisions about bot access, content strategy, and revenue diversification without clarity on which approach will prove sustainable.
A few weeks ago, I was given access to review a confidential OpenAI partner-facing report, the kind of dataset typically made available to a small group of publishers.
For the first time, from the report, we have access to detailed visibility metrics from inside ChatGPT, the kind of data that only a select few OpenAI site partners have ever seen.
This isn’t a dramatic “leak,” but rather an unusual insight into the inner workings of the platform, which will influence the future of SEO and AI-driven publishing over the next decade.
The consequences of this dataset far outweigh any single controversy: AI visibility is skyrocketing, but AI-driven traffic is evaporating.
This is the clearest signal yet that we are leaving the era of “search engines” and entering the era of “decision engines,” where AI agents surface, interpret, and synthesize information without necessarily directing users back to the source.
This forces every publisher, SEO professional, brand, and content strategist to fundamentally reconsider what online visibility really means.
1. What The Report Data Shows: Visibility Without Traffic
The report dataset gives a large media publisher a full month of visibility. With surprising granularity, it breaks down how often a URL is displayed inside ChatGPT, where it appears inside the UI, how often users click on it, how many conversations it impacts, and the surface-level click-through rate (CTR) across different UI placements.
URL Display And User Interaction In ChaGPT
Image from author, November 2025
The dataset’s top-performing URL recorded 185,000 distinct conversation impressions, meaning it was shown in that many separate ChatGPT sessions.
Of these impressions, 3,800 were click events, yielding a conversation-level CTR of 2%. However, when counting multiple appearances within conversations, the numbers increase to 518,000 total impressions and 4,400 total clicks, reducing the overall CTR to 0.80%.
This is an impressive level of exposure. However, it is not an impressive level of traffic.
Most other URLs performed dramatically worse:
0.5% CTR (considered “good” in this context).
0.1% CTR (typical).
0.01% CTR (common).
0% CTR (extremely common, especially for niche content).
This is not a one-off anomaly; it’s consistent across the entire dataset and matches external studies, including server log analyses by independent SEOs showing sub-1% CTR from ChatGPT sources.
We have experienced this phenomenon before, but never on this scale. Google’s zero-click era was the precursor. ChatGPT is the acceleration. However, there is a crucial difference: Google’s featured snippets were designed to provide quick answers while still encouraging users to click through for more information. In contrast, ChatGPT’s responses are designed to fully satisfy the user’s intent, rendering clicks unnecessary rather than merely optional.
2. The Surface-Level Paradox: Where OpenAI Shows The Most, Users Click The Least
The report breaks down every interaction into UI “surfaces,” revealing one of the most counterintuitive dynamics in modern search behavior. The response block, where LLMs place 95%+ of their content, generates massive impression volume, often 100 times more than other surfaces. However, CTR hovers between 0.01% and 1.6%, and curiously, the lower the CTR, the better the quality of the answer.
LLM Content Placement And CTR Relationship
Image from author, November 2025
This is the new equivalent of “Position Zero,” except now it’s not just zero-click; it’s zero-intent-to-click. The psychology is different from that of Google. When ChatGPT provides a comprehensive answer, users interpret clicking as expressing doubt about the AI’s accuracy, indicating the need for further information that the AI cannot provide, or engaging in academic verification (a relatively rare occurrence). The AI has already solved its problem.
The sidebar tells a different story. This small area has far fewer impressions, but a consistently strong CTR ranging from 6% to 10% in the dataset. This is higher than Google’s organic positions 4 through 10. Users who click here are often exploring related content rather than verifying the main answer. The sidebar represents discovery mode rather than verification mode. Users trust the main answer, but are curious about related information.
Citations at the bottom of responses exhibit similar behavior, achieving a CTR of between 6% and 11% when they appear. However, they are only displayed when ChatGPT explicitly cites sources. These attract academically minded users and fact-checkers. Interestingly, the presence of citations does not increase the CTR of the main answer; it may actually decrease it by providing verification without requiring a click.
Search results are rarely triggered and usually only appear when ChatGPT determines that real-time data is needed. They occasionally show CTR spikes of 2.5% to 4%. However, the sample size is currently too small to be significant for most publishers, although these clicks represent the highest intent when they occur.
The paradox is clear: The more frequently OpenAI displays your content, the fewer clicks it generates. The less frequently it displays your content, the higher the CTR. This overturns 25 years of SEO logic. In traditional search, high visibility correlates with high traffic. In AI-native search, however, high visibility often correlates with information extraction rather than user referral.
“ChatGPT’s ‘main answer’ is a visibility engine, not a traffic engine.”
3. Why CTR Is Collapsing: ChatGPT Is An Endpoint, Not A Gateway
The comments and reactions on LinkedIn threads analyzing this data were strikingly consistent and insightful. Users don’t click because ChatGPT solves their problem for them. Unlike Google, where the answer is a link, ChatGPT provides the answer directly.
This means:
Satisfied users don’t click (they got what they needed).
Curious users sometimes click (they want to explore deeper).
Skeptical users rarely click (they either trust the AI or distrust the entire process).
Very few users feel the need to leave the interface.
As one senior SEO commented:
“Traffic stopped being the metric to optimize for. We’re now optimizing for trust transfer.”
Another analyst wrote:
“If ChatGPT cites my brand as the authority, I’ve already won the user’s trust before they even visit my site. The click is just a formality.”
This represents a fundamental shift in how humans consume information. In the pre-AI era, the pattern was: “I need to find the answer” → click → read → evaluate → decide. In the AI era, however, it has become: “I need an answer” → “receive” → “trust” → “act”, with no click required. AI becomes the trusted intermediary. The source becomes the silent authority.
Shift In Information Consumption
Image from author, November 2025
This marks the beginning of what some are calling “Inception SEO”: optimizing for the answer itself, rather than for click-throughs. The goal is no longer to be findable. The goal is to be the source that the AI trusts and quotes.
4. Authority Over Keywords: The New Logic Of AI Retrieval
Traditional SEO relies on indexation and keyword matching. LLMs, however, operate on entirely different principles. They rely on internal model knowledge wherever possible, drawing on trained data acquired through crawls, licensed content, and partnerships. They only fetch external data when the model determines that its internal knowledge is insufficient, outdated, or unverified.
When selecting sources, LLMs prioritize domain authority and trust signals, content clarity and structure, entity recognition and knowledge graph alignment, historical accuracy and factual consistency, and recency for time-sensitive queries. They then decide whether to cite at all based on query type and confidence level.
This leads to a profound shift:
Entity strength becomes more important than keyword coverage.
Consistency and structured content matter more than content volume
Model trust becomes the single most important ranking factor
Factual accuracy over long periods builds cumulative advantage
“You’re no longer competing in an index. You’re competing in the model’s confidence graph.”
This has radical implications. The old SEO logic was “Rank for 1,000 keywords → Get traffic from 1,000 search queries.” The new AI logic is “Become the authoritative entity for 10 topics → Become the default source for 10,000 AI-generated answers.”
In this new landscape, a single, highly authoritative domain has the potential to dominate AI citations across an entire topic cluster. “Long-tail SEO” may become less relevant as AI synthesizes answers rather than matching specific keywords. Topic authority becomes more valuable than keyword authority. Being cited once by ChatGPT can influence millions of downstream answers.
5. The New KPIs: “Share Of Model” And In-Answer Influence
As CTR is declining, brands must embrace metrics that reflect AI-native visibility. The first of these is “share of model presence,” which is how often your brand, entity, or URLs appear in AI-generated answers, regardless of whether they are clicked on or not. This is analogous to “share of voice” in traditional advertising, but instead of measuring presence in paid media, it measures presence in the AI’s reasoning process.
LLM Decision Hierarchy
Image from author, November 2025
How to measure:
Track branded mentions in AI responses across major platforms (ChatGPT, Claude, Perplexity, Google AI Overviews).
Monitor entity recognition in AI-generated content.
Analyze citation frequency in AI responses for your topic area.
LLMs are increasingly producing authoritative statements, such as “According to Publisher X…,” “Experts at Brand Y recommend…,” and “As noted by Industry Leader Z…”
This is the new “brand recall,” except it happens at machine speed and on a massive scale, influencing millions of users without them ever visiting your website. Being directly recommended by an AI is more powerful than ranking No. 1 on Google, as the AI’s endorsement carries algorithmic authority. Users don’t see competing sources; the recommendation is contextualized within their specific query, and it occurs at the exact moment of decision-making.
Then, there’s contextual presence: being part of the reasoning chain even when not explicitly cited. This is the “dark matter” of AI visibility. Your content may inform the AI’s answer without being directly attributed, yet still shape how millions of users understand a topic. When a user asks about the best practices for managing a remote team, for example, the AI might synthesize insights from 50 sources, but only cite three of them explicitly. However, the other 47 sources still influenced the reasoning process. Your authority on this topic has now shaped the answer that millions of users will see.
High-intent queries are another crucial metric. Narrow, bottom-of-funnel prompts still convert, showing a click-through rate (CTR) of between 2.6% and 4%. Such queries usually involve product comparisons, specific instructions requiring visual aids, recent news or events, technical or regulatory specifications requiring primary sources, or academic research requiring citation verification. The strategic implication is clear: Don’t abandon click optimization entirely. Instead, identify the 10-20% of queries where clicks still matter and optimize aggressively for those.
Finally, LLMs judge authority based on what might be called “surrounding ecosystem presence” and cross-platform consistency. This means internal consistency across all your pages; schema and structured data that machines can easily parse; knowledge graph alignment through presence in Wikidata, Wikipedia, and industry databases; cross-domain entity coherence, where authoritative third parties reference you consistently; and temporal consistency, where your authority persists over time.
This holistic entity SEO approach optimizes your entire digital presence as a coherent, trustworthy entity, not individual pages. Traditional SEO metrics cannot capture this shift. Publishers will require new dashboards to track AI citations and mentions, new tools to measure “model share” across LLM platforms, new attribution methodologies in a post-click world, and new frameworks to measure influence without direct traffic.
6. Why We Need An “AI Search Console”
Many SEOs immediately saw the same thing in the dataset:
“This looks like the early blueprint for an OpenAI Search Console.”
Right now, publishers cannot:
See how many impressions they receive in ChatGPT.
Measure their inclusion rate across different query types.
Understand how often their brand is cited vs. merely referenced.
Identify which UI surfaces they appear in most frequently.
Correlate ChatGPT visibility with downstream revenue or brand metrics.
Track entity-level impact across the knowledge graph.
Measure how often LLMs fetch real-time data from them.
Understand why they were selected (or not selected) for specific queries.
Compare their visibility to competitors.
Google had “Not Provided,” hiding keyword data. AI platforms may give us “Not Even Observable,” hiding the entire decision-making process. This creates several problems. For publishers, it’s impossible to optimize what you can’t measure; there’s no accountability for AI platforms, and asymmetric information advantages emerge. For the ecosystem, it reduces innovation in content strategy, concentrates power in AI platform providers, and makes it harder to identify and correct AI bias or errors.
Based on this leaked dataset and industry needs, an ideal “AI Search Console” would provide core metrics like impression volume by URL, entity, and topic, surface-level breakdowns, click-through rates, and engagement metrics, conversation-level analytics showing unique sessions, and time-series data showing trends. It would show attribution and sourcing details: how often you’re explicitly cited versus implicitly used, which competitors appear alongside you, query categories where you’re most visible, and confidence scores indicating how much the AI trusts your content.
Diagnostic tools would explain why specific URLs were selected or rejected, what content quality signals the AI detected, your entity recognition status, knowledge graph connectivity, and structured data validation. Optimization recommendations would identify gaps in your entity footprint, content areas where authority is weak, opportunities to improve AI visibility, and competitive intelligence.
OpenAI and other AI platforms will eventually need to provide this data for several reasons. Regulatory pressure from the EU AI Act and similar regulations may require algorithmic transparency. Media partnerships will demand visibility metrics as part of licensing deals. Economic sustainability requires feedback loops for a healthy content ecosystem. And competitive advantage means the first platform to offer comprehensive analytics will attract publisher partnerships.
The dataset we’re analyzing may represent the prototype for what will eventually become standard infrastructure.
AI Search Console
Image from author, November 2025
7. Industry Impact: Media, Monetization, And Regulation
The comments raised significant concerns and opportunities for the media sector. The contrast between Google’s and OpenAI’s economic models is stark. Google contributes to media financing through neighbouring rights payments in the EU and other jurisdictions. It still sends meaningful traffic, albeit declining, and has established economic relationships with publishers. Google also participates in advertising ecosystems that fund content creation.
By contrast, OpenAI and similar AI platforms currently only pay select media partners under private agreements, send almost no traffic with a CTR of less than 1%, extract maximum value from content while providing minimal compensation, and create no advertising ecosystem for publishers.
AI Overviews already reduce organic CTR. ChatGPT takes this trend to its logical conclusion by eliminating almost all traffic. This will force a complete restructuring of business models and raise urgent questions: Should AI platforms pay neighbouring rights like search engines do? Will governments impose compensatory frameworks for content use? Will publishers negotiate direct partnerships with LLM providers? Will new licensing ecosystems emerge for training data, inference, and citation? How should content that is viewed but not clicked on be valued?
Several potential economic models are emerging. One model is citation-based compensation, where platforms pay based on how often content is cited or used. This is similar to music streaming royalties, though transparent metrics are required.
Under licensing agreements, publishers would license content directly to AI platforms, with tiered pricing based on authority and freshness. This is already happening with major outlets such as the Associated Press, Axel Springer, and the Financial Times. Hybrid attribution models would combine citation frequency, impressions, and click-throughs, weighted by query value and user intent, in order to create standardized compensation frameworks.
Regulatory mandates could see governments requiring AI platforms to share revenue with content creators, based on precedents in neighbouring rights law. This could potentially include mandatory arbitration mechanisms.
This would be the biggest shift in digital media economics since Google Ads. Platforms that solve this problem fairly will build sustainable ecosystems. Those that do not will face regulatory intervention and publisher revolts.
8. What Publishers And Brands Must Do Now
Based on the data and expert reactions, an emerging playbook is taking shape. Firstly, publishers must prioritize inclusion over clicks. The real goal is to be part of the solution, not to generate a spike in traffic. This involves creating comprehensive, authoritative content that AI can synthesize, prioritizing clarity and factual accuracy over tricks to boost engagement, structuring content so that key facts can be easily extracted, and establishing topic authority rather than chasing individual keywords.
Strengthening your entity footprint is equally critical. Every brand, author, product, and concept must be machine-readable and consistent. Publishers should ensure their entity exists on Wikidata and Wikipedia, maintain consistent NAP (name, address, phone number) details across all properties, implement comprehensive schema markup, create and maintain knowledge graph entries, build structured product catalogues, and establish clear entity relationships, linking companies to people, products, and topics.
Building trust signals for retrieval is important because LLMs prioritize high-authority, clearly structured, low-ambiguity content. These trust signals include:
Authorship transparency, with clear author bios, credentials, and expertise.
Editorial standards, covering fact-checking, corrections policies, and sourcing.
Domain authority, built through age, backlink profile, and industry recognition.
Structured data, via schema implementation and rich snippets.
Factual consistency, maintaining accuracy over time without contradictions.
Expert verification, through third-party endorsements and citations.
Publishers should not abandon click optimization entirely. Instead, they should target bottom-funnel prompts that still demonstrate a measurable click-through rate (CTR) of between 2% and 4%, since AI responses are insufficient.
Examples of high-CTR queries:
“How to configure [specific technical setup]” (requires visuals or code).
“Latest news on [breaking event]” (requires recency).
“Where to buy [specific product]” (transactional intent).
“[Company] careers” (requires job portal access).
Strategy: Identify the 10–20% of your topic space where AI cannot fully satisfy user intent, and optimize those pages for clicks.
In terms of content, it is important to lead with the most important information, use clear and definitive language, cite primary sources, avoid ambiguity and hedging unless accuracy requires it, and create content that remains accurate over long timeframes.
Perhaps the most important shift is mental: Stop thinking in terms of traffic and start thinking in terms of influence. Value has shifted from visits to the reasoning process itself. New success metrics should track how often you are cited by AI, the percentage of AI responses in your field that mention you, how your “share of model” compares with that of your competitors, whether you are building cumulative authority that persists across model updates, and whether AI recognizes you as the definitive source for your core topics.
The strategic focus shifts from “drive 1 million monthly visitors” to “influence 10 million AI-mediated decisions.”
Publishers must also diversify their revenue streams so that they are not dependent on traffic-based monetization. Alternative models include building direct relationships with audiences through email lists, newsletters, and memberships; offering premium content via paywalls, subscriptions, and exclusive access; integrating commerce through affiliate programmes, product sales, and services; forming B2B partnerships to offer white-label content, API access, and data licensing; and negotiating deals with AI platforms for direct compensation for content use.
Publishers that control the relationship with their audience rather than depending on intermediary platforms will thrive.
The Super-Predator Paradox
A fundamental truth about artificial intelligence is often overlooked: these systems do not generate content independently; they rely entirely on the accumulated work of millions of human creators, including journalism, research, technical documentation, and creative writing, which form the foundation upon which every model is built. This dependency is the reason why OpenAI has been pursuing licensing deals with major publishers so aggressively. It is not an act of corporate philanthropy, but an existential necessity. A language model that is only trained on historical data becomes increasingly disconnected from the current reality with each passing day. It is unable to detect breaking news or update its understanding through pure inference. It is also unable to invent ground truth from computational power alone.
This creates what I call the “super-predator paradox”: If OpenAI succeeds in completely disrupting traditional web traffic, causing publishers to collapse and the flow of new, high-quality content to slow to a trickle, the model’s training data will become increasingly stale. Its understanding of current events will degrade, and users will begin to notice that the responses feel outdated and disconnected from reality. In effect, the super-predator will have devoured its ecosystem and will now find itself starving in a content desert of its own creation.
The paradox is inescapable and suggests two very different possible futures. In one, OpenAI continues to treat publishers as obstacles rather than partners. This would lead to the collapse of the content ecosystem and the AI systems that depend on it. In the other, OpenAI shares value with publishers through sustainable compensation models, attribution systems, and partnerships. This would ensure that creators can continue their work. The difference between these futures is not primarily technological; the tools to build sustainable, creator-compensating AI systems largely exist today. Rather, it is a matter of strategic vision and willingness to recognize that, if artificial intelligence is to become the universal interface for human knowledge, it must sustain the world from which it learns rather than cannibalize it for short-term gain. The next decade will be defined not by who builds the most powerful model, but by who builds the most sustainable one by who solves the super-predator paradox before it becomes an extinction event for both the content ecosystem and the AI systems that cannot survive without it.
Note: All data and stats cited above are from the Open AI partner report, unless otherwise indicated.
