Sitecore is acquiring generative engine optimization (GEO) startup Scrunch for around $225 million, according to a Bloomberg report, adding AI answer-engine visibility to an enterprise content platform that has been quietly building toward a machine-readable web strategy.

Neither Sitecore nor Scrunch have confirmed the price. The deal marks one of the larger investments in the emerging GEO market: the practice of optimizing brand content so it surfaces in AI-generated answers rather than traditional search results.

Scrunch’s platform shows brands real-time signals about how they appear across various AI platforms, along with competitive analysis and technical audits. Its Agent Experience Platform, or AXP, is designed to deliver content in formats AI agents can read and use without disrupting the human experience. Notable clients include Lenovo, Skims, Headspace, and Penn State University.

“We’re at a pivotal moment where companies must rethink traditional digital strategies and accept that the internet must be written for machines to understand if we want humans to experience it,” Eric Stine, Sitecore’s CEO, said in a statement.

Scrunch CEO and cofounder Chris Andrew echoed the same urgency in his own statement. “By joining forces, we’re helping companies meet buyers where they are, moving beyond traditional SEO to win inside AI-generated answers,” he said. “That’s where Scrunch’s AXP is a critical advantage, delivering content in a format AI agents can read and use, without disrupting the human experience, allowing brands to become the trusted sources that power those answers.”

The GEO space is becoming increasingly competitive as brands seek visibility in the AI experiences where consumers are spending more time. Scrunch previously raised $26 million, including a $15 million Series A last summer led by Decibel, with participation from Mayfield, Homebrew, and others.

The deal logic is in the numbers. Scrunch told ADWEEK last year that conversion rates in AI search are three to five times higher than in traditional online search, citing its own data. “A visitor coming from AI search is buying faster than a traditional organic visitor,” Andrew said at the time. Independent verification of those figures was not provided.

Third-party research offers some corroboration. In research conducted by Akamai, AXP-enabled webpages saw a 364% lift in brand presence in responses to non-branded AI prompts and a 218% spike in citations appearing in AI responses.

Stine said the combination would allow brands to “show up with greater clarity, authority, and relevance so they can build trust, increase share of voice, and influence decisions early in the buying journey when it matters most.”

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A.G. Sulzberger, publisher of The New York Times, delivered a sharp rebuke to the artificial intelligence industry Monday, accusing major tech companies of building their AI products on “brazen theft” of journalism and calling on news organizations worldwide to push back before it’s too late.

In a speech at the WAN-IFRA World News Media Congress, Sulzberger argued that AI companies are systematically strip-mining news content without permission or compensation, hollowing out the very public square they claim to serve.

“Their hijacking of the public square is made possible by the original sin that animates their AI products — a brazen theft of intellectual property that has occurred at an unprecedented scale,” Sulzberger said in prepared remarks. “Tech giants strip-mine news websites without permission or compensation. They repackage these stolen goods as their own, siphoning off the audiences and revenue that otherwise would go to the news organizations that created this work.”

Sulzberger laid out what he called the four ingredients of AI: talent, compute, energy, and data. The first three are paid for — engineers earn tens or hundreds of millions, data centers cost hundreds of billions. But “data,” Sulzberger argued, is treated differently, seized without consent or compensation despite being equally essential.

The tech industry’s justifications — that innovation requires it, that facts can’t be owned, that “fair use” permits it, that licensing deals take too long — don’t hold up, Sulzberger said. He noted that five of the top 10 sites used to train leading language models belong to news publishers, and that OpenAI has acknowledged it would be “impossible to train today’s leading AI models without using copyrighted materials.”

The financial stakes are enormous. The six leading AI companies have a combined valuation of $11 trillion — more than three times the GDP of France. Private AI investment in the U.S. reached nearly $350 billion in 2025. Yet industry data suggests less than half of 1 percent of that investment goes to compensate the publishers whose content powers the technology.

The impact on news organizations is already measurable. The largest newspapers tracked by Comscore saw traffic drop more than 45 percent on average as the AI race intensified over the last four years. Meanwhile, Meta alone now makes eight times more in ad revenue than every newspaper on earth combined.

“The tech giants are fully aware of the implications of this shift,” Sulzberger said, quoting a Microsoft executive who wrote that “the open web was built on an implicit value exchange where publishers made content accessible, and distribution channels helped people find it. That model does not translate cleanly to an AI-first world.”

The Times publisher was careful to position his remarks not as anti-AI. He noted the Times uses AI internally — “responsibly, ethically, and with humans making the decisions” — to improve how it reports and distributes journalism. “Holding a powerful new technology at arms length is a recipe for failure,” he said.

But he pushed back hard on the idea that paying for content would cripple American competitiveness. “In its competition with China, America weakens itself by abandoning the intellectual property protections that fuel innovation and power America’s creative enterprises,” he said.

Sulzberger acknowledged the irony of a 175-year-old newspaper criticizing tech disrupters. But he argued the AI situation is different: the companies aren’t being disrupted by new technology — they’re the ones doing the disrupting, and they’re doing it on the backs of creators they’ve refused to compensate.

He urged the assembled news leaders from more than 60 countries to be more vocal. “Our profession has been too quiet, too passive and too fragmented in the face of abuses by the companies leading the AI revolution,” he said.

The speech ended with a plea for news organizations to stand firm on their value — and to stop pretending information wants to be free. “Information is valuable. Journalism is valuable,” Sulzberger said. “We cannot afford to be as naive this time.”

Edited by Pete Pachal

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For a while, it seemed like Google Search was in trouble.

Seemingly caught by surprise by the AI revolution that ChatGPT sparked, Google looked old and confused as upstarts like OpenAI and Perplexity pointed to a new future that replaced the “10 blue links” with question-and-answer conversations. Google’s first steps into this future were unsteady, with error-filled answers epitomized by the infamous glue-on-pizza moment. Some suspected, for all its scale and influence, a post-Google world was near.

That looks a lot less likely after this week. At Google I/O, the company confidently showed us its version of our informational future. And while it might be post-search, it’s not at all post-Google. Google is expanding its use of AI Overviews, meaning more searches will include the top-of-page summaries, and it’s adding a query box within them. When a user engages with it, they’re kicked to AI Mode, which abandons the “10 blue links” altogether.

In addition, oogle.com now has a “+” icon, similar to its Gemini chatbot. If user engages with it and uploads a file or photo, that will also take them to AI Mode. It’s now extremely difficult to search on a Google product without AI being part of the result. You can still find your page of links by switching to “Web,” though that option is often buried.

So, far from the future where search is competitive again, it’s increasingly looking like a new future that’s the same as the old future. Even if you look just at AI chatbots, the Gemini app is now at 900 million users, making it about as big as ChatGPT. That doesn’t even count AI Overviews and AI Mode, which have 2.5 billion and 1 billion users, respectively, according to the company.

The bots ARE the traffic

The obvious consequence of all this is more searches will begin and end in the query. For publishers, that continues and likely accelerates the ongoing traffic apocalypse. We may, however, have to update our vocabulary: Google Zero—which was supposed to connote an environment where the clicks from Google search were basically nil—feels imprecise.

That goes double when you consider that, as humans spend more time in AI interfaces, a commensurate amount of bot activity spreads out from those queries. So the future isn’t Google Zero. It’s Google Bot Infinity.

So the future is a world where people happily chat—either via typing or speech—to Google, and those Google bots bring the right information and context to answer them. More accurately, those bots bring what they deem as the right information and context to queries. AI systems prioritize information differently from traditional search, looking for information that both fits a pattern but also includes novel and authoritative elements. This is manifesting into the new-but-rapidly-evolving field of GEO, or generative engine optimization. Google’s renewed push into AI experiences means the battle for presence in answers is no longer a side bet. It’s the game.

That’s the media story here in Google’s renewed rise. Once laughed at for how far behind it was in the AI race, it’s now architecting the future where it’s still in charge. Judging by its balance sheet—with earnings steadily increasing even as competitors rise—it’s found the right balance of building the new while preserving the old. Even as it demotes the “10 blue links” that built the company, it’s offering a bevy of new ad products in conversational search that spin up generative ads on the fly. It clearly has the confidence that it can make money in an AI world.

Brands might be less confident about that, and publishers even more so. Authority in AI answers is nice, but monetizing has so far been a challenge.

Credibility is the new click

But it’s not nothing. If Google’s AI layer becomes the place where people encounter information, then presence inside that layer becomes a form of distribution. A publisher cited consistently in answers about politics, technology, health, finance, or culture has something valuable: proof that it owns authority in a category. The old metric was how many people Google sent to you. The new one may be how often Google needs you to make its answers credible.

That may not produce the same clean, scalable ad business that search referrals once did. But it points to a different one. Advertisers have always wanted to sit next to authority. They sponsored sections, bought podcast reads, backed newsletters, underwrote events, and cut direct deals with creators because association matters. If a publisher becomes one of the sources AI systems repeatedly rely on, that authority can be sold directly—not necessarily through Google, and not necessarily as a banner ad awkwardly stapled to a webpage.

That’s the hopeful version of Google Bot Infinity. Publishers may lose a lot of casual traffic, and pretending otherwise is foolish. But the ones that produce distinctive, trusted, deeply useful work still have leverage. The job now is to make that work legible to machines without making it lifeless for people.

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The era of the “ten blue links” is officially over.

Google unveiled a sweeping AI-powered overhaul of Search at its I/O conference Tuesday, TechCrunch reported, centered on what the company calls the biggest change to the search box in more than 25 years. Instead of returning a simple list of links, Google Search will drop users into AI-powered interactive experiences, starting this week.

The reimagined search box expands to accommodate longer, conversational queries without forcing users to pick a search mode at the start. A new AI-powered query suggestion system moves beyond autocomplete, helping users craft more complex queries. AI Overviews now allow follow-up questions in AI Mode, which launched last year and already has more than 1 billion monthly users.

The rise of information agents

Perhaps the most consequential change: users will be able to create, customize, and manage multiple “information agents” within Google Search starting this summer. These agents work in the background 24/7, tracking changes on the web and alerting users when conditions are met, pulling from real-time data and delivering synthesized updates.

It’s an evolution of Google Alerts, the change-detection service Google launched in 2003. “You could send an alert to track market movements in a particular sector with very specific parameters, and the agent will map out a monitoring plan for you, including the tools and the data it needs to access,” said Liz Reid, Google’s head of Search. “And it will then keep track of those changes and let you know when the conditions are met, and provide a synthesized update with links and information you can dive into further.”

The shift means “searching the web” will increasingly be performed by AI agents rather than humans. People will spend less time clicking links and more time acting on synthesized information. It’s a shift our coverage of the answer engine era has been tracking closely.

Generative UI and mini apps

Google is also introducing “generative UI”—building custom widgets and visualizations on the fly in response to users’ search questions. A query about black holes could generate an interactive visual that users can then ask follow-up questions about, with Google responding with brand-new visuals in real time. Search results will increasingly look like interactive web pages.

The system, built in partnership with Google DeepMind using Gemini Flash 3.5, will also let users tap into Google’s Antigravity platform to build personalized mini apps directly in Search using natural-language commands, such as meal-planning apps that factor in your calendar, fitness apps tailored to your goals.

AI Overviews now has more than 2.5 billion monthly users. Conversational search (AI Mode) tops 1 billion monthly users. For context, ChatGPT has 900 million weekly active users, suggesting ChatGPT sees more frequent repeated engagement, while Google reaches more unique people across its AI features in a month.

The publisher problem

Combined, these changes will likely deepen the toll on publisher referral traffic, which has already been decimated since AI Overviews launched. Some ad-dependent media operations have already been pushed out of business. The UK CMA has been pressuring Google to let publishers opt out of AI Overviews without losing search visibility, a request Google has yet to act on.

The new search box arrives this week. Generative UI rolls out free to everyone this summer. Information agents and mini-app building launch first to Google AI Pro and Ultra subscribers this summer, with broader free access planned for Spark and other AI features down the line.

Sundar Pichai framed it as an accessibility play. “Part of the reason we focus on delivering frontier models—highly capable, but also very efficient, fast, and at a lower price—is because we want to bring it to as many people as possible,” he said in a press briefing ahead of I/O.

For publishers, there is very little time left to adapt.

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The copyright fight between publishers and AI companies has many fronts, but the trickiest one comes down to a single word: outputs. Even if scraping feels indefensible, courts generally aren’t interested in punishing the scrapers unless the resulting product is doing measurable damage to the people whose work was taken. Civil claims especially need a clear line from the act to the injury.

The 2023 Sarah Silverman case is the textbook example. A group of authors including the comedian sued OpenAI for using their books without permission, and a judge later tossed several of the claims because the plaintiffs couldn’t point to specific outputs that were direct copies of their work. Knowing a large language model (LLM) ingested your writing isn’t enough on its own. You have to show the model is producing something that eats into your business.

Why outputs matter more than scraping in court

That evidentiary burden is part of why these cases struggle. Scraping happens silently, at machine speed, behind layers of infrastructure most publishers never see. The outputs of public-facing tools like ChatGPT, Gemini, and Perplexity are easy enough to inspect, but a much larger scraping economy operates outside that view.

It’s been an open secret for a while that AI companies pull data from third-party brokers, and media analyst Matthew Scott Goldstein recently put numbers to it. His report, covered in Digiday, identifies at least 21 companies, several backed by hundreds of millions of dollars, that routinely scrape publisher content without paying for it and sell their “data services” to customers that include OpenAI, Amazon, and even publishers like The Telegraph.

The report is essentially a map of what scraping looks like when no one stops it. Multimillion-dollar businesses, most of them obscure to readers, exist for the sole purpose of indexing publisher content and reselling it to bots and agents. The names won’t ring bells: Parallel AI, Exa, and Bright Data. And they aren’t hiding what they do. A recent Wall Street Journal profile describes Parallel AI as a platform “dedicated to servicing AI agents.” Goldstein calls it a “scraper company with better branding.”

Charlie Munger’s old line—show me the incentives, and I’ll show you the outcome—applies cleanly here. Between the losing streak in court and an administration that has openly waved off copyright concerns, the signal to AI companies and the brokers feeding them is unmistakable. Unauthorized scraping carries little risk, and the default settings of the system push toward more access, not less.

The bot-blocking decision every publisher faces

That setup leaves publishers between a rock and a hard place. Either you block bots as aggressively as your stack will allow, or you let them in. Letting them in feels like surrender, but it also ends the constant whack-a-mole and clears space to build a business that assumes AI will ingest and repurpose your work no matter what.

I’d argue those two stances aren’t as opposed as they look. Publishers should defend their copyright, but they also have to plan for a world in which AI engines are baked into how content reaches anyone. AI is now a distribution channel, a middle layer, and an audience all at once.

So what does a serious response to all this look like? Five components, in my view. Not every publisher will have the resources for all of them.

• Get better at blocking bots. IP protection takes both legal and technical effort. Most large publishers are nominally blocking bots, but doing it for real means going past the robots exclusion protocol, the polite instructions sites give bots and which bots regularly ignore. People Inc. CEO Neil Vogel has said his company has needed to become highly sophisticated at blocking unauthorized bots.
  
  Smaller publishers won’t have that level of resourcing, but technical partners exist, and infrastructure providers like Cloudflare have started shipping copyright-protecting defaults. Even when sophisticated blocking is out of reach, intel is not. Look at your bot traffic, but also audit the AI services themselves to see where your content has surfaced without permission.
• Practice good GEO. This one feels backwards at first. Whether or not bots have your permission, your content should still be readable to them. Access is binary, on or off. Ignoring generative engine optimization (GEO) just means your work is harder for every bot to parse, including the ones you’d want to let in.
  
  The case for GEO is practical. Scraping is happening, so you may as well compete inside the summaries and pick up whatever qualified traffic results. It also generates a paper trail for the audits in the previous bullet, which can support any future legal claim. And it becomes foundational if you ever build an in-house agent or MCP server on top of your content.
• Shift your business model. I’ve covered this at length before, so the short version. The Google-era model is shrinking, and any business built on monetizing anonymous traffic is shrinking with it. New revenue streams (events, subscriptions, data products, licensing) have to be cultivated. Easier said than done. Diversification has to become a religion for ad-dependent publishers, not a side project.

• Sue. Not realistic for every publisher. Going after OpenAI or Perplexity requires resources most newsrooms don’t have. But the Goldstein report effectively introduces a new set of potential defendants who have been mostly invisible until now. Given what they’re openly doing and the size of the market involved, it would be strange if more legal action didn’t follow.

• Lobby for regulation. Federal action looks unlikely in the current climate, but states are moving on AI policy, including transparency and disclosure rules around training data. Real progress may not require rewriting copyright law from scratch. Even something as simple as requiring bots to properly identify themselves would stop the impersonation that makes the current scraping economy possible.

Why agency matters more than victory

As bots keep “eating the internet,” it’s tempting to treat scraping as one more thing publishers just have to live with. Some of that resignation is earned. But inevitability is not the same as paralysis. In a world increasingly run by agents, publishers have to claim back some agency of their own. Protect what’s protectable, adapt where adaptation is the only path, and refuse to let the same companies that scraped your work also write the rules for what happens to it next.

A version of this column appears in Fast Company.

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AI-referred visitors aren’t just increasing. They’re more likely to convert.

In an analysis of 2.3 billion sessions (January 2024 to December 2025):

• Traffic from generative AI grew 796% in two years.
• AI visitors converted approximately 1.2 times higher than organic search and at a higher rate than any other “free” channel.
• Organic and direct still dominate (63% of sessions), while AI accounts for 0.18%.

What this means for marketers:

• AI is changing when users arrive and how ready they are to act.
• Visitors from generative AI often come after researching options, comparing vendors, and narrowing their choices. This suggests they are more likely to take action when they land on a site.
• At the same time, traditional channels like organic search and direct still drive the majority of early discovery.

WebFX breaks down the data.

Note: This report was updated in March 2026 to reflect expanded data from January 2024 through December 2025. Earlier versions of this study (January 2024–February 2025) reported that generative AI traffic grew 165 times faster than organic search. The updated analysis extends the dataset and timeframe.

Generative AI has become a strategic traffic channel

By 2025, generative AI traffic was no longer behaving like a one-time spike. Generative AI grew approximately 796% from January 2024 to December 2025.

The quarterly growth pattern also shows how the channel evolved, explaining why it now deserves strategic attention. Growth in 2025 unfolded in three distinct phases: early adoption, acceleration, and maturation.

• Phase 1: Early adoption (January to April 2025). YoY growth ranged from 1,101% to 1,835%, driven by early adopters integrating generative AI platforms into research behavior alongside traditional search.
• Phase 2: Acceleration (May to July 2025). May reached a peak of 3,431% YoY, followed by elevated growth through July. This period reflects broader adoption and increased frequency of AI-assisted research.
• Phase 3: Maturation (August to December 2025). Growth moderated into the 260%–889% range. Session volume remained elevated, while the rate of increase stabilized into a more consistent pattern.

These numbers indicate the channel is maturing and stabilizing.

Traffic share remains small, but strategically meaningful

In 2025, generative AI accounted for 0.18% of total sessions. The share remains modest, yet its sustained growth and measurable conversion activity elevate its strategic relevance.

Organic Search still remains a primary traffic channel, though, holding the second-highest market share at 27.12% and trailing only Direct. Together, the two make up more than 60% of website traffic.

Traffic distribution across channels changed measurably in 2025, reflecting users’ evolving search and discovery behavior. When taken together, the quarterly growth pattern and traffic-share data show that generative AI is no longer an experimental referral traffic source. It is measurable, sustained, and tied to revenue activity.

Takeaways for marketers: Manage generative AI as a defined traffic channel

Generative AI should now be tracked, benchmarked, and forecasted like any other revenue channel.

Here’s what marketers should do.

Track AI referrals separately

In GA4, create a dedicated channel grouping or source filter for traffic from generative AI platforms so it does not merge into generic referral buckets. Doing so lets you accurately examine quarterly trends.

Monitor channel share alongside volume

Track AI’s percentage of total sessions alongside raw session growth to understand how your acquisition mix is changing. Monitoring traffic share tells you whether AI is becoming an important contributor to your pipeline or simply expanding from a small base.

Evaluate quality with scale

Session growth alone doesn’t tell you how important a channel is. Review conversion events per user and assisted conversion paths to measure generative AI’s revenue influence.

If AI-assisted sessions are high-quality, which means they lead to conversion actions, it may justify deeper content optimization or increased efforts to improve your visibility. If traffic quality is inconsistent, you may need to adjust your targeting or landing pages.

AI visitors are buyers, not browsers

From 2024 to 2025, sessions from generative AI platforms increased 796% YoY, while conversions increased by 6,432% YoY.

When conversions grow faster than sessions, it means a larger share of visitors are turning into leads, customers, or taking other meaningful actions. Generative AI traffic is not only expanding its reach but also improving conversion efficiency.

Across industries, users referred by generative AI consistently converted at higher rates than organic search throughout 2025. Industries like SaaS and Retail saw AI referrals convert at more than 50%, while organic search conversions were between 20% and 30%.

AI traffic had fewer sessions per user than organic search in both 2024 and 2025. In 2025, AI visitors averaged 1.14 sessions per user compared to 1.18 for organic search.

This pattern suggests less back-and-forth exploration. Many AI-referred visitors have already begun evaluating options elsewhere:

• Inside AI platforms
• Review sites
• Industry publications
• Community forums

When these users reach a company website, they’re confirming pricing, specifications, credibility, or contact information.

Generative AI traffic combines conversion efficiency with rapid growth

Generative AI delivered 0.79 tracked interactions per user. In practical terms, that’s roughly eight tracked interactions for every 10 visitors arriving from AI platforms.

For context, organic search generated approximately 12 tracked interactions per 10 visitors.

High-intent channels such as Affiliates and Paid Search generated even more interactions per visitor, which implies that visitors coming from these channels are in the earlier stages of their research.

Generative AI outperformed Direct, Organic Social, Referral, Paid Social, and Display in terms of tracked interactions per visitor. This places the generative AI channel in the middle tier of conversion efficiency — competitive but not the most efficient or highest-converting.

On its own, midtier efficiency is not unusual. What distinguishes generative AI is the combination of:

• Approximately eight interactions per 10 visitors
• 796% YoY session growth
• No direct media spend

No other unpaid channel grew this quickly while still driving meaningful conversion activity. This combination reflects a growing share of visitors arriving through AI platforms with meaningful conversion activity.

What marketers should do: Treat AI as a high-intent channel

Generative AI functions as a prequalification tool for prospects. For this reason, AI traffic behaves more like bottom-of-funnel traffic than early-stage discovery.

The data suggests several shifts in digital strategy.

AI as a decision-stage channel

Visitors arriving from AI platforms are often validating options rather than beginning research. Landing pages that clearly present key information—such as pricing, specifications, comparisons, and proof points—align with the verification behavior of these visitors.

AI-driven visitors are more likely to convert when information is immediate and structured.

Shifts in performance measurement

AI visitors averaged fewer sessions per user than organic search in both 2024 and 2025, yet generated several interactions with visitors. If you measure performance primarily on session depth or repeat visits, AI traffic may appear weaker than it is.

Benchmarking AI performance against high-intent channels rather than informational organic queries provides more accurate context.

Changes to reporting and attribution models

With 796% YoY session growth and meaningful interactions per user, AI is no longer experimental traffic. Tracking it as a defined channel in dashboards, revenue reporting, and forecasting models provides better visibility.

Tracking referral sources from AI platforms separately will prevent their impact from being absorbed into “referral” or “other” categories.

Content alignment with confirmation behavior

AI-driven visitors frequently arrive to confirm pricing, review technical details, or assess credibility. Landing pages that provide clear pricing and technical information, boost brand credibility with proof points, and guide visitors to next steps align with this behavior.

As AI visibility increases, the ability to appear in AI-generated responses directly influences which brands receive this decision-stage traffic.

AI compresses research and changes how users engage on-site

Generative AI accounted for just 0.18% of traffic in 2025. While small, it’s unique: What sets it apart from other traffic sources is how AI-referred visitors behave when they land on a business’s website.

In 2025, generative AI recorded a 66.48% engagement rate and a 54.15% session conversion rate. Organic search, by comparison, recorded a 70.86% engagement rate with a 45.23% session conversion rate during the same period.

Their difference shows up in how concentrated the visitors’ intent appears to be.

Organic-driven sessions include a variety of intents. Visitors land on a brand website to conduct early research, casual browsing, comparison shopping, fill out a form, or make a purchase.

On the other hand, generative AI sessions are more likely to include a measurable action. That’s why its session conversion rate is high (54.15%).

In practical terms, a higher percentage of AI-referred visits result in form submissions, resource downloads, quote requests, or other conversion events within the same session.

For marketers, that suggests something important: AI-referred users may have done some research before they click through your site. By the time they land on your site through an AI-assisted search, they’ve already learned so much about their options and are not starting from scratch.

This trend affects how you design high-intent experiences for AI-assisted visits.

Action: Optimize for decisive visitors across channels

While generative AI traffic accounts for only a small fraction today, the behaviors seen — higher session-level conversion activity — also apply to other high-intent visitors, whether they arrive via organic search, paid search, or direct.

The objective is to optimize websites so that when visitors arrive ready to act, the process is streamlined.

Making the next steps obvious and simple

When someone lands on a product or service page, the next steps should be immediately clear. High-conversion pages often share several characteristics

• Reasonable form lengths
• Nonredundant form fields
• Strategically placed calls to action (CTAs)

Adjusting messaging for returning visitors

Not every high-intent visitor converts on the first visit. Some return to confirm or compare pricing, so some organizations personalize content for returning visitors instead of repeating introductory messaging.

If someone has already viewed technical specifications, they likely don’t need a brand overview. Messaging can be adjusted by adding excerpts from case studies to provide reassurance.

Small personalization changes can support that momentum without requiring a full redesign.

Reinforcing credibility during the decision-making process

High-intent visitors — including AI-referred users — often concentrate on decision pages. Product, pricing, and demo pages often display social proof such as:

• Testimonials
• Industry certifications
• Clear deliverables

ChatGPT dominates generative AI discovery

From 2024 to 2025, ChatGPT accounted for 82.6% of all generative AI traffic. The next-closest platforms — including Perplexity and Google Gemini — accounted for much smaller shares.

When combined, the top three AI platforms generated 96.9% of all AI-driven visits. In other words, AI discovery is not spread across dozens of tools. Instead, most AI discovery happens on just a few platforms.

This concentration suggests that optimization principles remain consistent across the landscape, requiring authoritative content, clear explanations, structured information, and credible sources. While ChatGPT currently represents the largest share of AI answers, other platforms continue to play specific roles.

That doesn’t mean other platforms are irrelevant. Perplexity continues to serve research-heavy queries, and emerging assistants from Google and Microsoft are still evolving.

Pro tip for marketers: Maintain platform-agnostic optimization

Although traffic is concentrated, the foundations of AI visibility are largely universal.

AI platforms tend to reference authoritative content, such as original research, expert explanations, and clear answers to specific questions. Well-structured pages also assist crawlers in finding, extracting, and citing information. This suggests that building content robust enough for any AI system to rely on is more effective than creating tool-specific content.

Monitor emerging platforms without overinvesting

Perplexity, Gemini, and Copilot still contribute smaller shares of traffic today. As generative AI evolves as a channel, the distribution of traffic may change.

AI adoption accelerated across B2B industries

Generative AI traffic growth in 2025 was not confined to SaaS or technology companies. Adoption accelerated across research-intensive B2B sectors.

In this dataset, Manufacturing, Professional Services, and SaaS accounted for roughly 35% of generative AI traffic in 2025. These industries often require buyers to carefully compare options, validate capabilities, and align stakeholders before inquiring.

Manufacturing and Heavy Equipment showed sustained acceleration into late 2025, while Professional Services experienced an early-2025 surge followed by stabilization. As quarterly growth stabilized overall, these industries continued to see sustained increases in AI-referred sessions, showing us that technical buyers are incorporating AI tools into procurement workflows.

Home Services followed a different trajectory. AI traffic in this category moved from negligible volume in early 2024 to steady, conversion-producing streams by late 2025.

While total session share remained modest in Home Services, AI-assisted visits showed conversion activities, suggesting that AI platforms power vendor discovery and assist with initial outreach. Total session share in the SaaS and Software industry also appears small compared to other industries and is likely due to larger datasets coming from other B2B sectors.

B2B buyers are shortlisting vendors before they visit your website

B2B buyers increasingly use AI platforms to compare vendors, review specifications, and narrow options before visiting company websites. By the time they visit your website, they are confirming details, not starting their research.

If your specifications, service descriptions, or case studies are not surfaced in AI-assisted research, buyers may never discover or consider your business. That makes visibility during their early comparison critical — vendors mentioned at this stage have a chance of getting evaluated.

Strategies for B2B visibility in AI-assisted research

B2B buyers use AI platforms to gather, compare, and shortlist options before visiting vendors’ websites and inquiring. To get their attention at this stage, you must have structured, authoritative content.

Publish comparison-ready documentation

Make product specifications, service packages, compliance details, and pricing models easy to find and easy to interpret.

Front-load key information at the top of your pages. In addition, ensure product specs and key details are consistent across pages so buyers and crawlers can easily find and understand them.

Use structured data to reduce ambiguity

Structured data (or schema markup) won’t guarantee citations, but it helps crawlers extract and summarize your content accurately. For many B2B organizations, useful schema markups include:

• Organization (brand identity signals)
• Product or Service (offer details)
• Offer (pricing and packaging structure when applicable)
• FAQPage (common validation questions)
• BreadcrumbList (site structure)

Use the types that match what you actually publish to make important details clear.

Use consistent naming so you can be cited correctly

Keep product names, categories, and terminology consistent across pages. Doing so increases the likelihood that AI-generated summaries will reflect your correct offerings and details.

Earn trust with expert-backed, proof-focused content

B2B buyers look for credibility signals, while AI-powered searches look for statements that they can reference. When applicable, incorporate insights from subject-matter experts, case studies, and data-backed comparisons into your content.

For example, a manufacturing supplier can publish an engineer-reviewed specification table comparing material tolerances, performance metrics, and compliance standards across product lines, along with a case study.

By providing specific, technical details, you’re improving both buyer trust and AI interpretability.

Audit how your brand appears in AI answers

Regularly check how your B2B business appears for high-intent queries on major AI platforms. AI visibility tools can help monitor and analyze a brand’s presence on ChatGPT and other major AI search experiences.

How to optimize for AI visibility in 2026

Generative AI has not replaced traditional traffic channels, with direct and organic search still dominating with 35.51% and 27.12% of total sessions, respectively, in 2025. However, generative AI platforms are increasingly influencing how online users evaluate vendors and make purchase decisions.

This shift suggests there are different ways for audiences to discover brands and services. Appearing in traditional search results remains essential, but being mentioned in AI-generated answers is critical to getting noticed and shortlisted.

Here’s how.

1. Prioritize traffic quality along with volume

As earlier sections showed, the AI-referred visitors often arrive at websites ready to take action. Instead of focusing only on session growth, monitoring the quality of traffic arriving from different channels with metrics such as:

• Conversion events per user
• Assisted conversions
• Engagement patterns

These metrics reveal which channels drive revenue, helping you identify the optimization efforts to prioritize.

2. Track generative AI visibility as a distinct channel

Creating a separate reporting view for generative AI traffic in analytics platforms makes it easier to evaluate their influence. As AI platforms become a measurable source of discovery, isolating that traffic makes it easier to evaluate their influence.

Monitoring referral sources from major AI tools and comparing how those visits behave compared to other channels can reveal which pages, resources, and topics are most frequently surfaced in AI-generated responses.

Over time, this analysis can reveal which pages, resources, and topics are most frequently surfaced in AI-generated responses.

3. Align SEO and GEO through a “double-dip” strategy

Rather than treating generative engine optimization (GEO) as a separate initiative, it can be integrated with existing SEO strategies.

Search engines still capture a large share of discovery traffic, while AI platforms increasingly shape how buyers validate their options during evaluation. Having a strong content strategy can support both your SEO and GEO efforts.

A strong content strategy can support both. As research expands beyond traditional search, brands that get cited are those that consistently provide helpful answers backed by first-party data and experience across discovery channels.

SEO-focused content helps brands appear during early research. The same pages — when structured clearly and supported with credible information — can become sources that AI systems can cite when users ask deeper questions.

This “double-dip” approach allows a single piece of content to contribute to both discovery and decision stages of the buyer journey.

This story was produced by WebFX and reviewed and distributed by Stacker.

The post Inside AI traffic’s 796% growth, and why it converts more ready-to-buy visitors appeared first on The Media Copilot.

For two decades, the playbook was clear: pick your keywords, optimize your metadata, build backlinks, climb the rankings. That playbook is dissolving.

When someone asks ChatGPT to recommend the best coverage of a breaking news topic, there is no ranking page. There are no blue links. There is a single, synthesized answer assembled from sources the model decided were authoritative. Your outlet is either in that answer or it isn’t, and the tactics that got you to the top of Google have almost nothing to do with whether AI cites you at all.

It’s called generative engine optimization, or GEO—and it’s why I’m launching The Media Copilot GEO Dinner Series, a set of small, focused gatherings for media and communications leaders who need to get up to speed fast. The first one is in Manhattan on April 21.

The rules of this new era of content discovery aren’t obvious. Traditional SEO rewarded volume: more pages, more keywords, more backlinks. GEO rewards something closer to reputation. AI models weigh topical authority, clarity of argument, and the consistency of how a source is described across the web—qualities that are difficult to game and even harder to measure with conventional analytics. As I wrote in Fast Company, AI isn’t just stealing your traffic. It’s stealing your authority.

For media companies, the implications are existential. For communications professionals, they’re urgent. The first impression of your organization increasingly happens inside an AI-generated answer you never wrote and can’t edit. Every day you aren’t structured for AI retrieval is a day your competitors’ narratives are the ones getting surfaced.

I’ve been covering this shift in The Media Copilot, and the more I report on GEO, the more convinced I am that the people who need this information most aren’t getting it in a format that’s actually useful to them. That’s why I’m partnering with Amanda Coffee, founder of Coffee Communications and a comms veteran with leadership roles at PayPal, Under Armour, and eBay, to host small, intimate dinners where media and communications leaders get a focused briefing on the state of GEO, practical frameworks they can bring back to their teams, and direct conversation with peers facing the same questions.

The kickoff dinner is April 21 in New York. Seats are limited, so be sure to grab a ticket while you can. If your job involves earned media, audience strategy, or brand visibility, this is the room you want to be in.

The post SEO took 20 years to master. GEO changes everything. appeared first on The Media Copilot.

For decades, “good enough” content worked. A well-optimized article, a competent explanation of a topic, or a detailed blog post could still earn rankings and drive organic traffic.

That era has ended. Today, authenticity and radical transparency set the competitive baseline for content that ranks and delivers measurable results to businesses.

With generative AI now embedded into nearly every content workflow, the cost and time of producing average content have collapsed. In fact, 90% of marketers report faster production speeds when using AI tools.

However, the dawn of the AI era didn’t kill SEO. It removed the economic advantage of being merely competent, and now brands that publish authentic data and information are the ones that compound authority. While brands that publish interchangeable content disappear into the noise. Here, WebFX examines why volume-based strategies no longer work and what defensible content looks like in practice.

Why volume-based content strategies now work against you

For most of the last decade, content marketing rewarded output. More pages meant more keywords. More keywords meant more visibility in search results.

As generative AI accelerates publishing across industries, search results increasingly contain large clusters of pages that target the same topics, satisfy the same intent, and follow near-identical structures.

So now, search performance increasingly depends on whether your pages add net new value to the ecosystem, not on how many pages you have on your website. Minimalism in content production is becoming a priority.

Several factors explain why increasing content volume alone may hinder organic rankings and visibility efforts.

1. AI-content saturation

Generative AI can automate or accelerate 60%-70% of the time spent on knowledge work, such as research, outlining, and drafting content. Considering the cost of using AI content generation tools, it is likely that other organizations are also using them to fast-track content generation.

This means the web is quickly flooding with identical content that doesn’t provide readers with much value. As a result, search engines may not rank such content well, and it may not earn meaningful visibility or traffic.

2. Topic cannibalization and internal competition

Volume-driven strategies introduce internal competition where multiple pages on your site compete for the same or closely related keywords. This phenomenon, known as keyword or topic cannibalization, forces search engines to treat multiple pages as a single page and reduces the likelihood that individual pages will rank and be visible.

3. Diminished signals of authority and uniqueness

With AI’s rise, the baseline quality of content, which encompasses useful structure, keyword coverage, and readability, is now easy to replicate. This diminishes its relative value as a ranking signal.

So now search engines and AI systems are increasingly depending on external signals, like backlinks, citations, structured data, and unique insights to break ties between many superficially similar pages.

4. Changing user behavior and intent fulfillment

Since 2023, click-through rates have declined as search behavior has changed. Third-party studies have observed that AI Overviews correlate with a 34.5% drop in click-through rates for top organic results.

Additionally, Pew Research Center analysis found that when an AI summary appears, users click on traditional links in just 8% of searches, compared with 15% when no AI summary is shown.

When AI answers appear directly in search results and chat-based tools, many users get what they need without clicking through to a website. They scan summaries, compare sources, and move on. The click happens later or not at all.

The new content mandate: In 2026, brands must operate like research firms

Generative AI has standardized the primary differentiators between high-quality and low-quality content.

So your content marketing plan must now be exceptional to drive measurable impact.

This approach involves operating more like an expert-led research organization and less like a traditional journalistic publisher.

Additionally, you also need to adapt your content to indicate contribution rather than coverage to keep up with the accelerated content production unlocked by generative AI.

These efforts are essential because search engines now don’t ask if a page adequately answers a query in order to rank it. Instead, traditional and AI-powered search engines, like Perplexity, rank pages based on what net new value your page adds to the content ecosystem.

This is why two pieces of content can appear equally complete, yet produce dramatically different outcomes over time.

To better understand this change, it helps to compare how “high-quality content” worked before widespread AI adoption with how it functions today.

How high-quality content is evaluated: Before and after AI

The following table compares how content used to rank versus how it now ranks in the age of widespread AI production.

What defensible content actually looks like in practice

Defensible content has one defining trait: If it disappeared from your site tomorrow, a competitor wouldn’t recreate it by prompting an AI tool. Not quickly. Not cheaply. And definitely not at scale.

You can establish content defensibility by creating your content around the following four main elements:

1. Proprietary data as a moat

First-party data has become one of the strongest signals of authority available to brands. This could be any of the following:

• Aggregated customer insights
• Internal performance benchmarks
• Longitudinal trend analysis
• Original surveys

Even when public datasets are involved, defensibility emerges through methodology, interpretation, and context. Two brands can analyze the same data and produce very different levels of authority depending on how insight is extracted and framed.

2. Novel frameworks as durable intellectual property (IP)

Frameworks turn insight into intellectual property. They provide internet users with a structured way of understanding insights. They’re the perfect replacement for simple, repeatable checklists that gen AI now replicates and replaces with summaries and overviews.

Unlike step-by-step guides or best-practice lists, frameworks organize complexity. They typically:

• Name a problem space
• Define categories and relationships
• Establish key dimensions
• Explain the decision criteria
• Provide a repeatable lens for analysis and decision-making

Frameworks endure because they minimize cognitive load. Once users understand them, they become reusable mental shortcuts that are easy to reference and attribute. From an AI perspective, frameworks provide structured concepts that models can reference without flattening.

3. Expert-led insight as differentiation

When AI can produce drives of content in just seconds, then unique insights and expert judgment become scarce.

So creating content with expert-led insights gives you the edge you need in the new content era. You can unlock this by grounding your content in lived experience, real scenarios, real constraints, and real consequences.

Expert-led insights reflect how someone who has seen outcomes unfold thinks about a problem, making it more valuable and impactful.

This works because generative AI excels at summarizing consensus. It performs poorly when insight requires judgment about what matters most, what to ignore, or why common advice fails in practice.

When expert insight is embedded directly into content through analysis, interpretation, and point of view, it creates differentiation that cannot be automated away.

4. Human connection as a trust signal

The oversaturation of AI-generated content on the internet has led users to be more critical of the content they consume.

Yes, users enjoy getting answers faster and more conveniently thanks to AI overviews and chat summaries. But they’re still looking for reassurance, which you can give them by conveying emotional intelligence and authentic storytelling.

Human connection makes the data, expertise, and frameworks you’ve conveyed so far believable and relatable to the people consuming your content. AI systems pick up on this, too.

Content that shows perspective, accountability, and context is easier to recognize as credible. Content that feels interchangeable is easier to summarize away.

How this shift changes SEO outcomes over time

Defensible content follows a different trajectory than content built for coverage. Rather than peaking briefly and fading, defensible content accumulates value because:

• Original data attracts citations
• Named frameworks earn mentions and brand references
• Expert-led insight builds recall and refrencability
• Human connections reinforce authenticity and strengthen credibility

Over time, these signals build authority around a source instead of dispersing it across individual pages. As a result:

• Rankings stabilize
• New content gains traction more quickly
• Visibility becomes easier to maintain
• Search performance becomes less volatile
• Algorithm updates have less impact
• Competitive pressure increases for others

How compounding authority reshapes discovery

Search systems evaluate sources across time, not just pages in isolation. When a brand consistently contributes original insight, that contribution strengthens entity-level signals that influence future visibility.

As AI-powered discovery expands, this effect accelerates. Large language models (LLMs) reference sources that demonstrate depth, consistency, and originality across related topics. Authority travels with the idea and the source behind it, extending visibility beyond individual rankings.

This creates layered discoverability. Your content starts appearing in traditional search results, AI summaries, and referenced explanations without relying on repeated keyword competition.

For brands consistently investing in content, this means SEO outcomes increasingly reflect cumulative decisions rather than isolated tactics, and here’s how:

• Content strategies centered on defensible assets build momentum over time
• Each new investment benefits from prior authority
• Each signal reinforces the next
• Performance becomes an outcome of structure rather than effort

This story was produced by WebFX and reviewed and distributed by Stacker.

The post AI didn’t kill SEO. It killed average content. appeared first on The Media Copilot.

The numbers are in, and they’re bad. A new analysis of web traffic to 10 major tech and media outlets finds that Google search referrals have collapsed since the company rolled out AI Overviews — the summaries that now appear above traditional search results and answer user queries without requiring a click.

SEO firm Growtika tracked Ahrefs data from early 2024 to January 2026. At their peak, the ten outlets combined received 112 million monthly visits from US Google users. By January of this year, that number had fallen to just under 50 million — a drop of more than 55 percent industry-wide.

For some outlets, the decline is nothing short of catastrophic.

Digital Trends went from 8.5 million clicks per month in March 2024 to 264,861 in January 2026 — a 97 percent collapse. The Verge, HowToGeek, and ZDNet each lost more than 85 percent of their Google-referred traffic over the same period. Wired lost 62 percent. Even Mashable, the analysis’s best performer, shed 30 percent of its traffic.

Growtika’s analysis notes that the four worst-hit publications now receive less combined monthly web traffic than the r/ChatGPT subreddit.

What’s driving it

The firm identifies three compounding factors: the rollout of Google AI Overviews beginning in mid-2024; algorithm changes that boosted Reddit in search rankings; and the growing number of users who skip Google entirely in favor of AI chatbots like ChatGPT and Perplexity.

The steepest declines came in mid-2025, when Google significantly expanded the range of queries that trigger an automatic AI summary. By July of last year, roughly 25 percent of all Google searches generated an AI Overview — meaning a quarter of all searches were being answered without a click.

Google pushed back on the analysis in a statement to Futurism, calling it “fundamentally flawed” for examining too small a sample and failing to account for seasonal traffic variation. The company also cited shifting audience preferences toward podcasts and forums. That defense will ring hollow for publishers whose traffic data tells a different story.

What it means for newsrooms

For journalists and editors focused on AI’s impact on journalism, the traffic story is often framed as a future threat. This data suggests it is a present reality.

Publishers built audience development strategies around search-optimized evergreen content — explainers, how-tos, reference pieces — that now trigger AI Overviews instead of clicks. That content, which represented a reliable traffic baseline for many outlets, has been effectively nationalized by Google’s summary layer.

The outlets hit hardest are tech-focused publications — the same ones most likely to have invested heavily in SEO-optimized evergreen content. News organizations with strong breaking news operations may have more protection, since real-time journalism still generates “Top Stories” placement that AI summaries can’t fully replicate.

But the broader lesson is stark: the deal that sustained digital media for a decade — produce content, get traffic from Google, sell ads against that traffic — is deteriorating faster than most newsroom business models have adapted to account for it.

The post Can’t ignore the data: Google’s AI Overviews have gutted news site traffic appeared first on The Media Copilot.

Ask journalists and PR pros what keeps them up at night about AI and you’ll typically variations on the same theme: the machines will reduce the cost of making content to the point where their jobs will look mostly redundant. Today’s models can already crank out competent articles and pitches (and plenty else), which makes the “human touch” feel less like a differentiator and more like a nice-to-have.

Yes, AI is automating huge portions of knowledge work, and the ultimate outcome for media and adjacent industries is far from settled. But that’s only half the disruption. The bigger shift is that AI is rapidly becoming the default way people discover information. Billions now get information through AI experiences—chatbots, synthetic digests, and products like Google’s AI overviews—instead of clicking through traditional search results.

So the real power question isn’t “Can AI write?” It’s: Who gets surfaced when AI answers? The logic of these systems—how they crawl the web, what they trust, how they summarize, which sources they elevate, and what they quietly ignore—will dictate how journalism and public relations function going forward. It will also dictate how they collide, collaborate, and coexist. Sometimes journalism and PR align, sometimes they’re in conflict, but either way, AI becomes the interface where those dynamics play out.

What I’m talking about, of course, is GEO (generative engine optimization)—or, more precisely, the incentives GEO creates. AI answers are increasingly treated as the internet’s new front door because they’re wildly popular (ChatGPT alone has almost a billion users) and still growing. Google’s AI Mode for search, which swaps the “10 blue links” for an AI conversation, is now prominent on both the Google homepage and the Chrome omnibox. Some are predicting it becomes the default sooner than you might think.

If that happens, and it sure looks like it will, it’ll be brutal for publishers (a subject for another column). It would also locks in the AI summary as the primary portal to information for… well, pretty much everyone. But here’s the twist: it also suggests that the doomsday future journalists and PR pros fear—where automation and slop are the only viable weapons—doesn’t quite fit.

Why stories beat keywords

The game hinges on how generative engines decide what matters. Both journalists and publicists want their narratives to become the raw material for AI answers—PR on behalf of clients, media for itself—because appearing in those answers is a form of authority. The comforting part for journalism: studies show AI portals prioritize journalistic content far above commercial content (like corporate blogs and brand sites).

That’s good for PR pros too, because a big component of their work is media relations. Picture a PR campaign’s goals and messaging as one circle, and a journalist’s editorial interests as another. Where those circles overlap is where outcomes happen. That overlap is the highest-probability path for both sides to influence the summary an answer engine produces.

Why? Because AI engines don’t behave like classic search engines. Search cared about keywords; AI looks for patterns. When a generative system sees the same narrative echoed across sites, domains, and social platforms, it gains confidence in the summary it’s constructing. Domain authority—the strength of a specific URL—still matters, but topical authority matters more.

Translated: If an AI engine sees that a site, outlet, or individual has covered the same topic consistently, from multiple angles, and gets cited elsewhere, it strengthens the authority signal. That can matter as much as, and sometimes more than, generic coverage from a major (Tier 1, in PR language) publication.

This shifts the publicist-journalist relationship in two important ways. First, specialized journalists who own a beat become more valuable. The same goes for focused publications, which makes trade and B2B outlets newly relevant. Second, relationships with journalists remain crucial in media relations, but they’re no longer the whole strategy. Authority in an AI world can be reinforced through corporate blogs, social channels, and other formats. Journalistic content may be prioritized, but everything else helps lock in the narrative the engine thinks it’s seeing.

The beat as your brand

Now flip it around. Journalists have to play the same game. Their work may get first crack in GEO, but if it isn’t distinctive, it won’t stand out from competitors. If it’s too broad or too thin, answer engines will reach for sources that are more specific and comprehensive. If it doesn’t address the questions people routinely ask AI, the system will simply route around it and summarize something else.

So yes: in an AI-shaped media ecosystem, it’s better to have a defined coverage area than to be a generalist. But that’s just the cost of entry. In the same way PR builds a narrative across platforms and formats, journalists need to think about how to build those, too.

Most journalists make their living by writing articles. But if you want answer engines to notice what you’re doing, it’s sensible to distribute those stories across formats and channels. A personal website or newsletter. Speaking at events. Publishing in formats that AI systems and their users are increasingly tuned to, such as short-form video, podcasts, and whatever comes next. The objective is straightforward: increase the surface area of the stories you’re telling, the stories people are asking about in ChatGPT, Google, and Perplexity. Building your personal brand around them is a bonus.

The irony of this is that AI originally promised to offload “content marketing” chores around content, like writing social copy and SEO headlines, which virtually no journalist wanted to do. But with GEO as the new reality, thinking beyond the single story is more important than ever. You have to keep thinking about how your reporting can be repackaged, reframed, and redistributed so the systems that summarize the world actually see it.

The upside is that there’s an inherently human throughline to all this. Generative engines hunt for patterns, but they reward uniqueness inside those patterns. And uniqueness is what humans still do best. For journalists, it’s the scoops, the unearthed facts, the reporting that creates new information instead of remixing old information. For PR, it’s still the person-to-person relationships that reliably connect clients to those stories. As AI reshapes how stories are found and told, the advantage won’t belong to whoever can publish the most. It will belong to whoever can tell the most distinct story—and get it into the places the machines are listening.

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