AI-driven search engines are reducing user clicks
Search is changing, fast. AI platforms like ChatGPT, Claude, and Google’s AI Overviews are giving users instant, summarized answers. No links. No clicks. Just answers. That’s efficient for the user, but brutal for publishers built on web traffic.
Ad revenue depends on visibility. No visibility, no clicks, no money. This is already hitting hard. Google’s advertising revenue grew to $348.15 billion in 2024, but the growth is slowing, down to 13.9% year-over-year, compared to 41.3% during the pandemic era. That tells you something. Demand is softening, and the model is under pressure.
It’s worse for publishers. DMG Media, a major UK publisher, reported an 89% collapse in click-through rates in September 2025. They directly blamed AI Overviews. Google confirms these summaries now show in about 30% of processed queries, with almost 60% of mobile queries being zero-click. These numbers aren’t theoretical, they’re real, and rising.
So where does that leave web-based business models? Ad-driven publishers are watching their business models erode in real time. If users don’t visit websites, they can’t trigger ad impressions, click affiliate links, or load sponsored videos. The web’s attention economy is changing, and the click is no longer king.
Executives running ad-funded platforms should be looking closely at this. Even companies like Google, who engineered much of the web’s monetization, are having to reassess their fundamentals. And when the platform that sets the rules starts to adjust its engine, everyone else following that path has a lot more to lose, unless they move early.
Paywalled and subscription-based publishers face declining conversion rates
Subscription models are under stress, and AI is making it worse. When platforms like Google use publisher content to feed AI-generated Overviews, most users never leave the results page. That means they never reach the paywall, never see a call to subscribe, and never engage with the brand. Silent losses.
This is a major blind spot. Users get answers, but they don’t see where the answers came from. That breaks the standard value exchange, no visibility, no opportunity to convert a passive reader into a paying subscriber. Even The Guardian has reported drops in traffic of up to 79% for top-ranked articles pushed below AI Overviews. When your best-performing content gets buried by AI, your business feels it immediately.
There’s more. Subscription fatigue is real. People are managing multiple subscriptions across services, news, video, productivity. And when AI gives you what you need in a few seconds, how compelling is another $10 a month for long-form text content?
That’s the question executives at large media companies need to answer, and fast. For publishers with serious subscription ambitions, the response can’t just be “more paywalls.” Behind a paywall, content becomes invisible to AI; but out in the open, it fuels clickless AI summaries.
This balancing act is critical. Go too far with access restrictions, and you lose reach. Go too open, and you get harvested. C-suite decision-makers should start seeing proprietary data and deeply differentiated content frameworks not just as editorial wins, but as commercial lifelines in an ecosystem where being middle-of-the-road means disappearing.
The move now is clear: deliver content that AI can’t easily replicate, integrate services that add tangible user value, and build systems that convert visitors directly. Because the people who aren’t thinking about this yet? They won’t have a business to convert in the near future.
Attributing engagement and measuring the impact of sponsored content
AI isn’t just changing how content is discovered, it’s making attribution murky. When AI platforms scrape, summarize, and repackage publisher content into instant responses, it’s often stripped of trackable links or branding. Sponsored stories and affiliate-driven content lose their ability to prove impact. You can’t measure what users saw if they never land on your domain.
That’s a core issue for any executive responsible for marketing or branded media. If users engage through AI summaries and never click through, who gets the credit? Not the original creator. Engagement appears to come from the AI provider, not the source of the insights. This eliminates performance data that publishers and advertisers rely on, pageviews, conversions, dwell time, behavioral segmentation. Gone.
As AI systems expand their presence across the info economy, traditional measurement models, based on impressions, conversions, referral traffic, are degrading. This isn’t hypothetical. Attribution gaps are already driving a credibility problem across the content chain. Brand teams can’t tie investment outcomes to specific publishers when traffic is routed through large language models.
For sponsors and media buyers, the risk is inefficiency. Sponsoring a campaign across multiple publishers makes sense when you can see performance by property. That logic breaks when measurement stops at the AI response level. It gets harder to justify spend when you can’t prove impact on your KPIs.
This is where leaders need to adapt measurement frameworks. Tools like brand lift studies, direct code activations, or AI-ready metadata tagging may replace traditional formats. We’ll need new metrics tied to influence, visibility, and assisted engagement. If you’re running brand or growth for a media company or large-scale campaign, start testing now. If you wait for standards to emerge, you’re already late.
Search engines are grappling with the tension from AI features
Search engines face a disruptive trade-off. The pressure to integrate AI and remain competitive is high. So are the stakes. Their revenue model, especially Google’s, is built on clicks. PPC and display advertising don’t work without user interaction. But AI-generated answers reduce the need to click. That’s a problem.
Google’s leadership delayed launching full AI features for years. Not because they couldn’t, but because rolling them out too early would cannibalize their core business. The moment Google gives you answers without sending traffic, its own ad ecosystem takes a hit. Now with tools like ChatGPT, Perplexity, and Claude gaining traction, the market is forcing their hand.
AI Overviews, introduced by Google, already appear in around 30% of searches. As more queries resolve without leaving the results page, click-through rates decline, and revenue per search drops. This wasn’t unexpected, but it’s accelerating faster than many inside the ecosystem anticipated.
Bing and other search players face similar constraints. Microsoft has been quick to integrate OpenAI’s tech because it has less to lose than Google in the ad dominance race. Smaller search providers like DuckDuckGo or Brave are even more flexible, experimenting faster and with lower risk.
If you’re running product or strategy for a search platform today, you’re managing a difficult shift. You need to evolve your monetization model while maintaining dominance in distribution. That’s not operationally simple. Large companies with established systems face real inertia. But if they don’t redesign the business around AI functionality, they won’t control the next phase of the market.
This shift won’t just impact general search. It will extend to maps, shopping, and news, any category where the engine delivers quick answers. Every part of the search stack needs a rethink. C-suite executives at these companies must allocate capital toward business model redesign, not just AI R&D. AI isn’t just a product feature, it’s the catalyst for a structural reconstruction of their core monetization mechanics.
Publishers must evolve away from sole reliance on ad impressions
The ad impression game is shrinking. If fewer people visit your site, you’re showing fewer ads. If affiliate links get stripped out of AI summaries, or never appear, your conversions drop. Clicks drive revenue, and zero-click search is undercutting the foundation of traditional web publishing economics.
This is precisely why publishers now have to build on broader foundations. Winning in an AI-influenced ecosystem means making your content harder to abstract and easier to monetize across different channels, not just the open web. Exclusive tools, paid analysis, proprietary data, these need to become core products, not nice-to-have add-ons.
The urgency is clear. Video ad inventory is declining, not because users don’t want content, but because fewer users are navigating to pages that host it. CPMs fall when inventory rises but traffic falls, a poor trade-off for any team managing publisher revenue. Meanwhile, affiliate revenue models depend on product links embedded in editorial. If users don’t click through to read that editorial, affiliate programs dry up. AI skips the context and presents the recommendation.
C-suite leaders on the publisher side must consider partnerships with AI providers, refined first-party data strategies, and diversified product lines that go beyond just words on a page. Unlike previous cycles that rewarded scale and SEO, the next cycle will reward differentiation, data control, and brand loyalty. If your team can’t explain how you monetize influence without requiring a pageview, you’re not ready.
Don’t expect AI adoption to slow down either. It’s going to accelerate. That means revenue replacement planning should already be underway. Asset-light models, licensing, paid integrations, and value-per-impression strategies trained on influence, not traffic, those are the levers. Waiting to stabilize under legacy models is the wrong move.
Search engines will need to transition from click-based models
Click-based ads built Google. But that model is starting to fail as AI integrates deeper into daily search behavior and users get what they need without leaving the results page. When you eliminate the need to click, the advertising structure designed around those clicks loses its power.
Traditional PPC is efficient when tied to high intent. But as AI begins to dissolve the distinction between high-intent and informational queries, the volume and targeting of those ads decline. Even display advertising sees lower returns because publishers in the Google Display Network are experiencing reduced traffic, resulting in fewer ad calls, fewer impressions, and ultimately, lower revenue for Google and its partners alike.
Now is the point where search engines have to close the value gap. Premium AI subscriptions, feature-based upgrades, embedded advertising within AI summaries, these are the arrows left in the quiver. Google can’t wait until click erosion reaches full scale to act. Monetization has to evolve in parallel with product deployment.
AI-native advertising is currently unformed. But it will solidify quickly. OpenAI is expected to integrate ads. Perplexity is already testing embedded sponsored responses. Meta is planning to commercialize its AI interactions. Search incumbents must act fast to define this space, or the next dominant format will emerge without them at the table.
For top-level strategy leaders at these engines, this shift demands internal restructuring. Monetizing an AI product requires new sales ops, partnership models, and user tracking frameworks. Institutional inertia may slow the shift, but it’s essential. Funding this shift goes well beyond marketing experiments, it’s about sustaining dominance where the old model offers diminishing returns.
Flexibility is now a leadership trait, not a product feature. The engines that move with speed, and rethink the fundamentals of economic engagement, will define the AI interface market. Everyone else will play on someone else’s platform.
Different levels of AI adoption
AI’s rise isn’t linear, it’s accelerating. The speed of adoption across different platforms and user behaviors will create distinct phases of disruption, each demanding its own response. At the current 30% level of AI query integration, we’re already seeing measurable impact. Informational and “how-to” sites are losing traffic due to AI summaries dominating visible search space. Users get fast answers, and the link becomes irrelevant.
In this early stage, subscription models maintain some resilience, as readers still interact with deeper analysis or exclusive content. But performance-based marketing is tightening. Display ad performance is already weakening, and affiliate revenue is declining alongside it.
At 55% AI adoption, we move into a phase where more than half of search behavior no longer results in site visits. Most informational queries will be resolved before a site even loads. Mid-sized publishers, especially those relying on SEO and syndication, face existential risk. The economics of running independent outlets begin to break down. Ad-based revenue contracts by 40–60%, and subscription models weaken because users never reach gated content.
When AI dominates over 85% of queries, the entire structure of traditional publishing resets. Content licensing becomes standard. Publishers transform from destination platforms into back-end suppliers feeding AI systems. Trust-based brands with historic loyalty, proprietary research, or essential journalism might stay visible, but mass-market generalist publishers could fade.
For C-suite executives in publishing or search, the takeaway is straightforward: treat each adoption phase as its own era, not just a step on a continuum. The strategies that work at 30% won’t save you at 55%, and the tools you build for 55% won’t protect you in an 85% environment. Investment cycles in AI readiness, revenue model redesign, and enterprise-level content licensing need to begin early. Late adaptation isn’t manageable, it’s a non-option.
Emerging AI-native monetization models are on the rise
Traditional web monetization models are losing momentum. AI-native monetization is already in motion. The platforms leading this shift aren’t just innovating with technology, they’re changing the mechanics behind revenue.
Meta is integrating its LLM-powered chatbot with its advertising business. Conversations with the AI can influence which ads users see, even across different experiences. That changes how advertisers think about targeting. CNN reported this direction clearly: “Meta will soon use your conversations with its AI chatbot to sell you stuff.”
Perplexity started testing embedded ads in November 2024. OpenAI is expected to follow. AI-generated responses will effectively become new inventory, contextual, intent-rich, and surfaced in real time. For publishers and advertisers, this creates an entirely new space for engagement but also introduces a new dependency: the AI platform itself becomes the gatekeeper.
Licensing is the other side of the equation. Large publishers like News Corp saw this early and signed deals with OpenAI in May 2024. These agreements don’t just protect visibility, they create direct revenue flow for high-value content. The next wave will be defined by companies that own proprietary data, deep research, or exclusive access, content AI can’t generate on its own.
OpenAI, for instance, is projected to generate over $20 billion in annualized revenue, according to CNBC (Nov. 6, 2025). That’s mostly from subscriptions and enterprise partnerships, not advertising, yet. But the path to integrated ads is clear. The economic model is already morphing, even as scale is still unfolding.
The core message for senior leaders is this: AI is not simply a delivery upgrade, it’s a distribution platform. And distribution platforms create new economic systems. Publishers, brands, tech companies, anyone who creates content, must now think about monetization that is embedded, not linked. Content needs to be valuable enough to license, adaptive enough to integrate, and distinct enough not to be replaced. That is the business case for surviving the next version of the web.
Recap
This shift isn’t theoretical, it’s already picking up speed. AI is changing how people access information, how value moves across the web, and how revenue is generated. The link economy that once supported ads, affiliates, and subscriptions is no longer guaranteed. Traffic doesn’t mean what it used to.
For business leaders, the priority now is clarity. Look at your models and ask what survives in a zero-click world. If your value is only realized after a user visits your platform, then you’re exposed. The companies that will lead the next cycle won’t just produce content, they’ll own proprietary data, differentiated experiences, and relationships that AI can’t replicate or replace.
Executives at tech platforms, media companies, and brands should be investing in influence metrics, licensing strategies, AI-native monetization tests, and more integrated ecosystems. Depend too long on legacy models, and you’re building in descending relevance.
The playing field is already moving. The market winners will be the ones who move with it, and build what’s next, not protect what worked.
