The New York Times added 280,000 digital-only subscribers in the second quarter of 2026, down from the 310,000 it added in the first three months of the year. As search traffic declines, the company is increasingly turning to video to reach and retain audiences.

The slowdown came during a quarter packed with news, including the Iran war, the FIFA World Cup and four Pulitzer wins, Nieman Lab reported.

CEO Meredith Kopit Levien pointed to the changing economics of the web.

“We delivered our Q2 results against the backdrop of a rapidly changing information ecosystem shaped by a small number of big tech companies whose moves continue to result in less traffic to publishers,” she said in prepared remarks for the company’s earnings call. “The Times isn’t immune to that impact.”

That’s a notable admission from a publisher that has long been viewed as one of the best positioned to withstand the decline in search traffic.

Smaller news organizations have been warning about falling referrals from Google and other platforms for years. The Times has had a significant advantage: 13.3 million total subscribers and a direct relationship with readers that many publishers lack. The company says it remains roughly on track to reach 15 million subscribers by the end of 2027. Total subscription revenue rose to $538 million in the quarter.

But the Times is increasingly looking beyond search to reach audiences — and video is a big part of that strategy.

Kopit Levien described the company’s video investments as “long-term bets.” They helped push adjusted operating costs up 10% from a year earlier. The Times hired eight video journalists in January and currently lists 12 open positions focused on video.

The company added a Watch tab to its flagship app last year and launched a Shows tab this week for longer-form franchises spanning news, opinion, culture and lifestyle.

Much of that programming puts Times reporters in front of the camera to explain their own reporting. Kopit Levien called the format “inherently humanizing and trust building.” The company now produces thousands of videos each quarter.

Asked about the financial return from the video push, Kopit Levien said video has played a “minor role” in advertising revenue so far. The company’s priority, she said, is to “really focus on scaling production, scaling engagement, and then scaling monetization.”

The Times did not disclose engagement figures, making it difficult to assess how well the investment is working from the outside.

There’s another cost worth watching: the company’s legal fight over generative AI.

The Times spent $4.6 million on generative AI-related litigation during the quarter, bringing its total to $32.9 million since it began breaking out those costs in early 2024. For the country’s most successful digital news publisher, litigation over the use of journalism to train AI systems has become a recurring, eight-figure expense.

For other newsrooms, the Times’ results point to an uncomfortable reality that even scale may not be enough to offset the loss of search traffic.

The Media Copilot has tracked declining search referrals across the industry, and the Times’ experience suggests the problem is not limited to smaller publishers. Its response — building direct distribution through video on its own platforms — requires substantial investment and may take years to monetize.

That leaves smaller publishers looking for cheaper ways to accomplish the same thing.

The audience logic is straightforward. Pew Research Center data shows Americans increasingly getting news from video-first platforms, particularly younger audiences.

The bigger question is whether the Times can turn that audience into paying subscribers quickly enough to justify the cost of building a video operation — before the economics of digital publishing shift again.

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ChatGPT users on the mobile app saw roughly twice as many ads per hour this July as they did in April, according to Business Insider. The jump coincides with a sharp expansion in the pool of advertisers buying those slots.

US data from market intelligence firm Sensor Tower estimates OpenAI has nearly tripled its advertiser count on ChatGPT, from about 300 in April to more than 820 in July. At least 160 advertisers joined in July alone.

The advertiser mix has shifted too. Tech companies including Speechify, Jotform and Natural Intelligence dominated the top ranks in April. By July they had fallen out of Sensor Tower’s top 10, replaced by Booking Holdings, Intuit, Home Depot and L.L.Bean.

Scheels, a sports clothing retailer, was the only company to sit in the top 10 in both months.

The shift extends beyond advertiser count. Retail still accounts for the largest share of ChatGPT ad spending, but its dominance is slipping as financial services, travel and tourism, and jobs and education gain ground.

Financial services has grown the fastest, rising from 2% of ad spend in April to 12% in July.

Sensor Tower identified BestMoney.com as one of ChatGPT’s top advertisers in July, saying the broader mix suggests OpenAI is diversifying its advertiser base — a potentially positive sign for monetization.

The numbers remain a fraction of OpenAI’s ambitions. The company reported $100 million in annual recurring ad revenue in May — a rounding error next to Google’s $81.6 billion and Meta’s $59.3 billion in ad revenue in their most recent quarters.

OpenAI serves ads to users on its free tier and its $8-a-month Go plan, which together account for the vast majority of a user base Sensor Tower estimated at 1 billion monthly app users in May.

OpenAI ads chief Dave Dugan told Business Insider in June that the company initially kept ad placements deliberately limited to protect the user experience. Four months in, he said, the team has grown more confident about scaling up. For now, ads appear beneath ChatGPT’s answers, while OpenAI avoids placing them alongside queries about personal health or politics.

For publishers and media buyers, the bigger story is not today’s ad revenue but where the next wave of attention is forming. A chatbot with a billion users putting ads beneath its answers creates a new pool of inventory competing with search and social for the same budgets.

OpenAI’s self-serve ad platform, launched in May, remains rudimentary compared with the mature systems run by Google and Meta. That makes the early surge in big-brand spending look more like testing than a long-term commitment.

For newsrooms already watching referral traffic decline as readers turn to chatbots instead of clicking links, the stakes are higher. The way OpenAI builds out its ad business could shape where publishers reach audiences — and how they get paid — as search behavior shifts.

OpenAI’s next steps include expanding into new markets and adding targeting tools, both designed to pull more advertising dollars into an app most people still use for free. The bigger question is whether advertisers stick around once the novelty wears off. July’s numbers suggest growing interest, but not yet lasting commitment.

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A Google search for “Burnham social care” on July 30 surfaced something new at the top of the page: BBC, Guardian and Caring Times articles stacked in a Top Stories carousel directly inside an AI Overview, according to Press Gazette.

The placement is part of a broader shift in how Google handles breaking news in AI-generated search results—and it creates a new dilemma for publishers weighing whether to opt out of Google’s AI features.

Google began rolling out the embedded Top Stories carousel in June. When it appears, it replaces the standalone Top Stories box that traditionally sits below an AI Overview, putting publisher links closer to the top of the page instead of forcing users to scroll through AI-generated text to find them.

The format is already showing up at meaningful scale.

Data from news SEO firm Newzdash shows that 15.5% of trending-news searches in the US that triggered Top Stories displayed the module within an AI Overview rather than in its traditional position.

The analysis covered more than 17 million Top Stories appearances across 40-plus countries during the 30 days ending July 28. So far, Newzdash has observed the embedded format only in the US and UK.

Entertainment was the category most likely to trigger the placement, accounting for 35.1% of cases in the US and 31.5% in the UK World news and sports followed, with sports appearing more often in the UK than in the US.

Newzdash founder John Shehata described the change as a “double-edged sword.” Google has said it wants to make links more prominent in its AI search experiences, and embedding the full Top Stories carousel does exactly that.

“We do not yet have comparative clickthrough data, but it should create a stronger opportunity for clicks than pushing Top Stories further down the page,” Shehata said.

But the new placement comes with a catch: Publishers may have to accept AI-generated summaries of their content to qualify for it.

Breaking news was once relatively insulated from Google’s AI answers. That protection is fading. Nearly half of Google search results pages now carry an AI Overview, according to Semrush’s tracker, putting news publishers increasingly inside the AI-generated search experience.

Google recently added Search Console controls allowing publishers to exclude their content from AI Overviews and AI Mode. The UK rollout followed a ruling by the Competition and Markets Authority.

Newzdash found that Top Stories appeared either inside an AI Overview or as a standalone module — never both. That means publishers that opt out of Google’s generative AI features could lose access to the embedded placement, with no guarantee that Google will show the standalone Top Stories module elsewhere on the page.

Shehata said publishers should distinguish between two different Google controls.

Blocking Google-Extended in robots.txt restricts the use of content for model training and grounding and should not affect a publisher’s eligibility for Top Stories. The Search Console exclusion is different: It determines whether a publisher’s links can appear in Google’s AI features at all.

The trade-off lands amid a growing debate over whether publishers should block their content from AI search. Opting out can keep a publisher’s content from being summarized by Google, but it may also remove the publisher from one of the most prominent referral opportunities Google has introduced in years.

And in an AI search environment where being cited by name increasingly matters, the embedded Top Stories carousel is more than a technical change. It is a new strategic choice for news publishers: accept a place inside Google’s AI experience, or risk disappearing from one of its most visible news placements.

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UK publisher digital revenue fell 4.55% in the first quarter of 2026, ending four straight quarters of growth, according to the Digital Publishers’ Revenue Index run by the Association of Online Publishers and Deloitte.

The survey, reported by Press Gazette, covered 13 publishers, nine business-to-consumer and four business-to-business. Richard Reeves, managing director at the AOP, called the drop the “first tremors in an earthquake being felt across the industry” as AI-generated answers and shifting audience habits keep readers from clicking through to source sites.

The decline was uneven. Some 62% of respondents still reported revenue growth, the highest share in a year, which the AOP said points to the heaviest losses being concentrated among a minority of publishers most exposed to changes in the information ecosystem.

The numbers that fell fell hard. Recruitment classified revenue dropped 44.84%, other classified was down 38.17% and off-platform revenue fell 20.27%. The AOP attributed much of that to people getting what they need from AI answers without visiting a publisher. Digital audio revenue sank 46.98% year on year for the second quarter running.

The click-through data backs up the concern. Separate AOP research found only 26% of ChatGPT users say they would click at least one media link in a response, rising to 34% for a Google Search carrying an AI Overview. Users are 18% less likely to click through to a source when an AI Overview sits at the top.

The “miscellaneous” category, covering revenue that doesn’t fit the main buckets, posted the single biggest decline. The AOP suggested AI substitution of publisher content across referral channels was a factor. It also flagged agencies Dentsu and WPP pulling out of The Trade Desk’s OpenPath initiative over concerns about ad placement and hidden fees.

Not everything went down. Digital display advertising rose 5.06%, likely on the back of more direct-sold premium deals. Every respondent now ranks advertising a high business priority, up from 75% a year earlier. Online video grew 1.29%, sponsorship 0.91% and subscriptions a thin 0.63%.

Reeves said the display turnaround “must be commended and demonstrates the quality of the advertising product that premium publishers provide.” Andy Cowen, lead partner for telecoms, media and entertainment at Deloitte, said the display growth shows the value premium content still holds but warned the drops elsewhere point to an “urgent need for publishers to adapt.”

For newsrooms, the report reads as a warning about cost. Every publisher in the index now says it will prioritise cost reductions, up from 50% a year ago, and every one says acquisitions are a priority, up from 25%. That mirrors wider pressure across the sector, where journalism’s workforce is shrinking as AI and new consumer habits reshape the industry.

There is one thread publishers can pull on. AOP research found a reader’s trust in an AI answer depends on their perception of the news brand cited in it, which underlines how much AI companies still rely on verified publisher content even as they starve those publishers of clicks. Whether that dependency translates into deals or dollars is the next thing the revenue index will measure.

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Nine Entertainment, Australia’s largest locally owned media company, will cut around 30 newsroom jobs at the Sydney Morning Herald and the Age, blaming what its publishing chief called an “extreme state of disruption” caused by AI.

Tory Maguire, Nine’s managing director of publishing, delivered the news to staff on Tuesday, according to reporting from The Guardian. The reductions will come through a mix of voluntary and targeted redundancies. Maguire framed the move as an evolution rather than a straight cost cut, though financial pressure is evident.

“In the last 12 months, as we have discussed many, many times at the publishing town halls, we are in an extreme state of disruption because of AI,” she told staff.

Maguire put AI ahead of every prior shock the industry has absorbed. The internet and social media, she said, did not disrupt the business as much as AI is now.

The numbers behind that claim are becoming clearer. The Reuters Institute’s Digital News Report 2026 found 10% of news consumers worldwide now use AI chatbots to get their news, rising to 16% among people under 35. Meanwhile, fewer readers are clicking through from search engines, because Google increasingly answers queries with its own AI summaries instead of sending traffic to publishers.

Australian outlets face a compounding problem. Research from the University of Sydney found local journalism was largely invisible in AI-generated news summaries produced by Microsoft Copilot, which overwhelmingly surfaced US and European sources instead.

Nine’s own financials reflect the squeeze. Digital subscription revenue fell 6% last year, and Maguire said reader revenue stalled for the first time in three years, with the division under enormous pressure partly due to the popularity of AI search results. The company says it plans to retrain existing staff and hire for what it describes as digital-first, reader-savvy, data-informed roles.

The Australian Financial Review is exempt from this pressure. Maguire credited its head start, pointing to a paywall it has run since 2007, as evidence the AFR is further along in adapting than the two legacy mastheads.

This is not an isolated incident. Two years ago Nine cut 200 jobs, including 90 from the mastheads and the AFR, and in 2024 chief executive Matt Stanton warned of $100m in cuts over two years. That earlier round, tied to Meta abandoning the news media bargaining code, triggered industrial action and a wave of senior departures. The merger with Fairfax Media in 2018 shed another 144 roles.

For newsrooms elsewhere, the pattern here is worth watching closely. Publishers are now naming AI not as a future risk but as the immediate cause of headcount decisions, and the referral economy that funded much of digital journalism is thinning fast.

Whether retraining and new digital roles can offset the loss of search and social traffic is the open question. Nine is betting they can. The next set of subscription numbers will show whether that bet is paying off.

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Organic Google Search referrals to eight major UK publishing groups fell 7.1% between the first and second quarters of 2025. If that holds, the volume of pageviews Google sends to these publishers will be cut in half by the third quarter of 2027.

That projection comes from the Association of Online Publishers’ new Artificial Intelligence Publisher Impact Study, detailed by Richard Reeves in Press Gazette. The AOP commissioned DJB Strategies to analyze six months of traffic across 10.8 billion pageviews, roughly 80% of the participants’ total after long-tail URLs were stripped out, alongside an Ipsos survey of consumer AI use.

The study revealed the decline even as Google reported that its Search revenue grew 12% and expanded AI Overviews, as the company turns Search from a route to content into a destination that answers queries directly. With Google pushing its zero-click AI Mode toward the center of the search experience, the study’s authors expect the drop to speed up rather than level off.

News publishers held up better than most, averaging a quarterly decline of around 5%, against 18% for the consumer and B2B segment. The study attributes that to tighter guardrails on news inside AI Overviews compared with the evergreen content that AI aggregates easily. Split by topic, news actually ticked up 2%. Sports, travel and health & wellness gained 37%, 22% and 7% respectively, while technology, finance & business and politics fared worst, falling 28%, 23% and 7%.

Guide and how-to content lost more than 20% of its Google referrals, exactly the kind of query a reader gets from an AI answer instead of clicking through. Content carrying an explicit point of view resisted substitution best. Opinion and commentary referrals rose 4%, interviews rose 1%, and product reviews stayed roughly flat.

The catch is that reportage, the largest single content category, accounted for more traffic than everything else combined and still slipped 4%. The study suggests finding ways to inject reportage with a more distinctly human voice, which lines up with the broader argument that authority is becoming the new speed as generic information retrieval moves to the machines.

Sites funded largely by high-volume programmatic advertising rely the most on transient search traffic and have the least cushion. Reader-funded operations, however, can absorb the shock.

The study’s warning is blunt: don’t chase falling traffic by pumping out more URLs in the hardest-hit categories. That only dilutes views-per-page and accelerates the losses. The volume game built on basic information retrieval is ending, and Google is the one turning out the lights. The publishers who survive will be the ones producing work an AI answer can’t cleanly replace.

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For the first time in the internet’s history, bots account for more web traffic than humans.

Cloudflare CEO Matthew Prince announced the milestone this week, according to Tom’s Hardware, noting that automated traffic has now eclipsed human-generated requests online, months ahead of even his own projections.

“Welp, that happened faster than I predicted,” Prince wrote on X. “Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.”

According to Cloudflare’s Radar data, bots represented roughly 57% of all HTTP requests as of late April 2026, with humans accounting for the remaining 43%. Bot traffic has held between 53% and 60% in the weeks since. Prince said the actual crossover occurred in the last few months, though the data is messy enough that pinning down an exact date is difficult.

The shift underscores how quickly AI agents have transformed web traffic patterns. Before the generative AI era, bot traffic sat at around 20% of all web activity, with Google’s web crawler serving as the largest single source. Now, AI agents performing tasks on behalf of users are generating requests at a scale that dwarfs human browsing behavior.

Prince illustrated the contrast at SXSW earlier this year: “If a human were doing a task—let’s say you were shopping for a digital camera—you might go to five websites. Your agent or the bot that’s doing that will often go to 1,000 times the number of sites that an actual human would visit. So it might go to 5,000 sites. And that’s real traffic, and that’s real load, which everyone is having to deal with and take into account.”

The reaction to Prince’s announcement was swift. Tech billionaire Elon Musk replied with a single “Wow” to the post.

The full picture is more nuanced. While bots now dominate HTML request traffic—reading pages, scraping content, indexing sites—humans still account for roughly 65% of total web activity when the metric expands to include app usage, video streaming, maps, and social media scrolling. Bots have overtaken humans in the specific act of navigating and reading the web, but not in the broader measure of people actually using the internet.

Cloudflare, which handles approximately one-fifth of all global web traffic, has been tracking the trend closely. The company’s 2026 Threat Intelligence Report also found that bots now account for 94% of all login attempts across its network, meaning only 6% of login attempts come from actual humans.

The crossing point Prince initially forecast for 2027 arrived in 2026. What once required a two-year runway happened in a matter of months.

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Google is not a company anyone expected to root for in the AI era. The early Bard demos were rough, Perplexity and ChatGPT were peeling away curious users, and antitrust regulators were closing in. The narrative a year ago was that the search giant had finally been outmaneuvered.

Now that looks dead wrong. Google is in a much stronger position today. Not because of it’s just coming off a prolific I/O developer conference, and not because it suddenly has the best model or the most capable AI ecosystem. Those titles get passed around the big labs every few months in any case. The reason is simpler: the money is still coming in.

The business is adapting

The Q1 numbers tell the story. Alphabet’s Google Services revenue was up 16% to $89.6 billion. Google Search and “Other” revenue was up 19%. The data is a strong indicator that the supposed AI disruption to its search products hasn’t dented the ad machine. If anything, it has fed it.

That confidence showed at I/O. Google announced many new AI products, but one of the most notable ones to the media industry was a set of new ad formats. Conversational Discovery ads are built on the fly to fit naturally into the answer to the person’s query, appearing as a “sponsored” section. Highlighted Ads and AI-powered Shopping Ads work similarly inside general product category queries. And then there are Business Agents for Leads, tailored versions of Gemini that live inside the ad itself.

These formats are still in testing, but the direction is obvious. Google is getting more sophisticated about how it monetizes AI experiences. A few months ago, the company stated it had no plans to sell ads in Gemini, a line executives floated in response to ChatGPT ads. Technically that line is still operative; Google can still say that the Gemini chatbot is not becoming an ad product. But that distinction feels less meaningful now that so many Gemini-powered AI experiences across Search are being commercialized.

Here is the part publishers should sit with. All those AI-powered ads appear within or next to an answer. That answer is built, in large part, from the work of media publishers. In the old system, Google sold ads next to results, and those ads benefitted from the close proximity to links from trusted media sources. Search the best SUVs and you may see ads for Toyota or Hyundai before you see a link to Car and Driver.

Now the information, built in part from the publisher’s content, is right there on the result. The user gets the info, the AI-powered ad provides a path to transact, and everything is handled without any need for them to ever leave Google. The shift is fundamental. Instead of monetizing the path to information, Google is now monetizing the information experience itself.

Publishers, of course, get cut out of that bargain. In many cases, their content was the raw material that informed the answer. Early in the AI search era, Google’s pitch to publishers was that AI-referred traffic was higher quality, more likely to engage and transact. That was, broadly, true. But why would users engage on a publisher site when Google is providing the means to do that before they ever arrive? The new ad formats are an acceleration of a trend that was already bad for publishers.

Trust is the variable everyone is missing

And yet. Users don’t care about business models. Whether they have an inclination to buy something or engage depends not just on the content of the answer but on how much they trust it. That is where the calculus gets interesting for publishers. A study published in Nature described trust in AI as dynamic and context-dependent. In other words, it changes depending on the nature of the AI experience and over time. A separate study by the Reuters Institute found users had moderate trust in AI answers, but they also value their speed and aggregation. Translation. Utility is high. Trust is conditional.

One of the most important assets any media brand has is the trust it cultivates over time. Imagine two AI answers about the same product. One built from social posts, blogs, Reddit threads, and online forums. The other built from articles on Consumer Reports, the Wirecutter, Time, and CNET. The user doesn’t need to know the methodology to feel the difference. Which one sounds more trustworthy?

Citations, in other words, are not decoration. People will be more inclined to trust answers created from brands that they’re familiar with. Hard data on AI ad performance is thin, but the entire media ad model is founded on this idea. An ad doesn’t just benefit from being present on a platform. It benefits from being associated with that platform’s brand. Ads inherit context. They always have.

Google has not, to date, been especially responsive to what publishers want. But Google does need advertisers to believe AI search ads work. That need is the leverage. If advertisers see better performance when ads appear beside credible, well-sourced answers, they will care about the quality of those answers. Once advertisers care, Google has to care. That could create pressure on Google to maintain a healthier source ecosystem.

What that pressure looks like is the open question. It may not look like simple licensing deals. It could involve clearer traffic paths, richer citation treatment, new publisher products, commercial partnerships, or advertiser demand for premium source environments inside AI search results. Each of those is a different commercial conversation publishers should be having now, not later.

The click fades but the value doesn’t

Review sites are the clearest example because the transaction path is obvious. If someone asks for the best dishwasher, the AI answer can cite reviews and then push the user toward purchase. But the same logic extends well past commerce. A health answer, a travel plan, or even a summary of a political issue all depend on source trust. Even when there’s no immediate checkout, the user’s confidence in the answer shapes what they believe and what they do next.

For publishers, the warning is straightforward. Google’s new push into AI ad experiences could further weaken traditional publisher revenue streams, especially traffic-based display, affiliate, and search-driven monetization. For practitioners trying to think a step ahead, there is another side to the equation. If AI answers need credibility to be useful, then credible media still has value. That value will not always show up on a referral chart. But it will still shape whether users trust the answer enough to act on it.

A version of this column appears in Fast Company.

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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, Google.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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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.

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