LLM Traffic Is Shrinking via @sejournal, @Kevin_Indig

LLM referral traffic has been growing +65% year-to-date. But we should assume 0 in the future.

LLM Referral Traffic Is Shrinking

LLM referral traffic in B2B grew +65.1% since January – but dropped -42.6% since July.

Image Credit: Kevin Indig

My December prediction of 50% organic by 2027 is dead:

• In December 2024, I analyzed six B2B sites and found LLM referral traffic was growing at such a fast rate it would make up 50% of organic traffic in three years.
• Today, I’m finding the monthly growth rate of LLM traffic dropped from 25.1% in 2024 to 10.4% in November 2025.
• Even from January to July 2025, the average growth rate was lower (19.2%) than my projection. That’s fast, but not enough to reach 50% organic traffic in three years.

LLM contribution to organic traffic grew from 0.14% in 2024 to 1.10% in 2025, which is more than I projected (0.79%).

Image Credit: Kevin Indig

But with organic traffic falling due to AI Overviews, this growth becomes meaningless.

Fewer Citations Despite Growing Usage

In August, several factors influenced LLM referral traffic:

1. Seasonality: Siege Media documented that B2B sites lost LLM traffic in August due to vacation season.
2. Router: ChatGPT 5, which launched on August 7, has a router that picks the model. The router favors non-reasoning models, which show fewer citations and send less traffic out.
3. Concentration: Josh from Profound found a higher concentration of referrals to Reddit and Wikipedia starting late July.

Business seasonality has a lower impact because neither ChatGPT (consumer focus) nor Claude (business focus) sees a decrease in site visits.

Image Credit: Kevin Indig

Image Credit: Kevin Indig

ChatGPT mentions, however, dropped by one-third in October and continue dropping in November.

Image Credit: Kevin Indig

Citations for large domains like Reddit or Wikipedia follow suit (based on Profound data).

Major sites see citation declines in September (Image Credit: Kevin Indig)

Conclusion: LLM visits are up, which removes seasonality as dominant cause. The driver of lower referral traffic is ChatGPT, showing fewer citations due to the model router.

Visibility Is The Real Price

Traffic was never the right way to value LLMs because LLMs make clicks redundant:

• The AI Mode study I published last month validates that clicks only occurred for shopping-related tasks (zero-click share = ~100%).
• Pew Research has found that only 1% of users click links in AI Overviews.

Focusing on traffic leads to disappointing results. ChatGPT is more like TikTok than Google Search. The currency of the AI world is visibility.

The good news: LLMs grow the pie. Semrush found people don’t use Google less often because they also use ChatGPT. If LLMs are additive to Google Search, the visibility surface grows even though clicks per source shrink. You have more places to be seen, fewer clicks per place.

But our success metrics need to change. Referral traffic neither works for ChatGPT nor Google, as AI Overviews and AI mode swallow more clicks. Instead, we need to adopt visibility-first.

Default To Zero LLM Traffic

1. Track LLM and organic search seasonality for your vertical to measure the total pie of citations and make sense of drops/spikes.
2. Monitor total citation and mention count to answer the question, “Are we growing because the market grows?” Lower citations/mentions means fewer chances to influence purchase decisions.
3. Prioritize brand mentions over citations in LLMs. Mentions without links drive familiarity and influence purchase decisions.
4. Stop expecting (meaningful) LLM referral traffic. Budget for visibility.
5. Invest resources where LLMs go to train: UGC and third-party reviews like Reddit, YouTube, review sites, community forums.

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Featured Image: Paulo Bobita/Search Engine Journal

How Agentic Browsers Will Change Digital Marketing via @sejournal, @DuaneForrester

The footprint of large language models keeps expanding. You see it in productivity suites, CRM, ERP, and now in the browser itself. When the browser thinks and acts, the surface you optimize for changes. That has consequences for how people find, decide, and buy.

Microsoft shows how quickly this footprint can spread across daily work. Microsoft says nearly 70% of the Fortune 500 now use Microsoft 365 Copilot. The company also reports momentum through 2025 customer stories and events. These numbers do not represent unique daily users across every product; rather, they signal reach into large enterprises where Microsoft already has distribution.

Google is pushing Gemini across Search, Workspace, and Cloud. Google highlights Gemini inside Search’s AI Mode and AI Overviews, and claims billions of monthly AI assists across Workspace. Google also points to customers putting Gemini to work across industries and reports average time savings in Workspace studies. In education, Google says Gemini for Education now reaches more than 10 million U.S. college students.

Salesforce and SAP are bringing agents into core enterprise flows. Salesforce announced Agentforce and the Agentic Enterprise, with updates in 2025 that focus on visibility and control for scaled agent deployments. SAP positioned Joule as its AI copilot and added collaborative AI agents across business processes at TechEd 2024, with ongoing releases in 2025.

And with all of that as the backdrop, should we be surprised that the browser is the next layer?

Image Credit: Duane Forrester

What Is An Agentic Browser?

A traditional browser shows you pages and links. An agentic browser interprets the page, carries context, and can act on your behalf. It can read, synthesize, click, fill forms, and complete tasks. You ask for an outcome. It gets you there.

Perplexity’s Comet positions itself as an AI-first browser that works for you. Reuters covered its launch and the pitch to challenge Chrome’s dominance, and The Verge reports that Comet is now available to everyone for free, after a staged rollout.

Security has already surfaced as a real issue for agentic browsers. Brave’s research describes indirect prompt injection in Comet and Guardio’s work, and coverage in the trade press highlights risks of agent-led flows being manipulated.

Now OpenAI has launched ChatGPT Atlas, a browser with ChatGPT at the core and an Agent Mode for task execution.

Why This Matters To Marketing

If the browser acts, people click less and complete more tasks in place. That compresses discovery and decision steps. It raises the bar for how your content gets selected, summarized, and executed against. Martech’s analysis points to a redefined search and discovery experience when browsers bring agentic and conversational layers to the fore.

You should expect four big shifts.

Search And Discovery

Agentic flows reduce list-based searching. The agent decides which sources to read, how to synthesize, and what to do with the result. Your goal shifts from ranking to getting selected by an agent that is optimizing for the user’s preferences and constraints. That may lower raw click volumes and raise the value of being the canonical source for a clear, task-oriented answer.

Content And Experience

Content needs to be agent-friendly. That means clear structure, strong headings, accurate metadata, concise summaries, and explicit steps. You are writing for two audiences. The human who skims. The agent that must parse, validate, and act. You also need task artifacts. Checklists. How to flows. Short-form answers that are safe to act on. If your page is the long version, your agent-friendly artifact is the short version. Both matter.

CRM And First-Party Data

Agents may mediate more of the journey. You need earlier value exchanges to earn consent. You need clean APIs and structured data so agents can hand off context, initiate sessions, and trigger next best actions. You will also need to model events differently when some actions never hit your pages.

Attribution And Measurement

If an agent fills the cart or completes a form from the browser, you will not see traditional click paths. Define agent-mediated events. Track handoffs between browser agent and brand systems. Update your models so agent exposure and agent action can be credited. This is the same lesson marketers learned with assistants and chat surfaces. The browser now brings that dynamic to the mainstream.

What To Do Now

Start With Content

Audit your top 10 discovery and consideration assets. Tighten structure. Add short summaries and task snippets that an agent can lift safely. Add schema markup where it makes sense. Make dates and facts explicit. Your goal is clarity that a machine can parse and that a person can trust. Guidance on why this matters sits in the information above from the Martech article.

Build Better Machine Signals

Use schema.org where it helps understanding. Ensure feeds, sitemaps, Open Graph, and product data are complete and current. If you have APIs that expose inventory, pricing, appointments, or availability, document them clearly and make developer access straightforward.

Map Agent-First Journeys

Draft a simple flow for how your category works when the browser is the assistant. Query. Synthesis. Selection. Action. Handoff. Conversion. Then decide where you can add value. This is not only about SEO. It is about being callable by an agent to help someone finish a task with less friction.

Rethink Metrics

Define what counts as an agent impression and an agent conversion for your brand. Tag flows where the agent initiates the session. Set targets for assisted conversions that originate in agent environments. Treat this as a separate channel for planning.

Run Small Tests

Try optimizing one or two pages for agent selection and summarize ability. Instrument the flows. If there are early integrations or pilots available with agent browsers, get on the list and learn fast. For competitive context, it is useful to watch how quickly Atlas and Comet gain traction relative to incumbent browsers. Sources on current market share are below.

Why Timing Matters

We have seen how fast browsers can grow when they meet a new need. Google launched Chrome in 2008. Within a year, it was already climbing the charts. Ars Technica covered Chrome’s 1.0 release on December 11, 2008. StatCounter Press said Chrome exceeded 20% worldwide in June 2011, up from 2.8% in June 2009. By May 2012, StatCounter reported Chrome overtook Internet Explorer for the first full month. Annual StatCounter data for 2012 shows Chrome at 31.42%, Internet Explorer at 26.47%, and Firefox at 18.88%.

Firefox had its own rapid start earlier in the 2000s. Mozilla announced 50 million Firefox downloads in April 2005 and 100 million by October 2005, less than a year after 1.0. Contemporary reporting placed Firefox at roughly 9 to 10% market share by late 2005 and 18% by mid-2008.

Microsoft Edge entered later. Edge originally shipped in 2015, then relaunched on Chromium in January 2020. Edge has fluctuated. Recent coverage says Edge lost share over the summer of 2025 on desktop, citing StatCounter.

For an executive snapshot of the current landscape, StatCounter’s September 2025 worldwide totals show Chrome at about 71.8%, Safari at about 13.9%, Edge at about 4.7%, Firefox at about 2.2%, Samsung Internet at about 1.9%, and Opera at about 1.7%.

What This History Tells Us

Each major browser shift came with a clear promise. Netscape made the web accessible. Internet Explorer bundled it with the operating system. Firefox made it safer and more private. Chrome made it faster and more reliable. Every breakthrough paired capability with trust. That pattern will repeat here.

Agentic browsers can only scale if they prove both utility and safety. They must handle tasks faster and more accurately than people, without introducing new risks. Security research around Comet shows what happens when that balance tips the wrong way. If users see agentic browsing as unpredictable or unsafe, adoption slows. If it saves them time and feels dependable, adoption accelerates. History shows that trust, not novelty, drives the curves that turn experiments into standards.

For marketers, that means your work will increasingly live inside systems where trust and clarity are prerequisites. Agents will need unambiguous facts, consistent markup, and licensing that spells out how your content can be reused. Brands that make that easy will be indexed, quoted, and recommended. Brands that make it hard will vanish from the new surface before they even know it exists.

How To Position Your Brand For Agentic Browsing

Keep your approach simple and disciplined. Make your best content easy to select, summarize, and act on. Structure it tightly, keep data fresh, and ensure everything you publish can stand on its own when pulled out of context. Give agents clean, accurate snippets they can carry forward without risk of misrepresentation.

Expose the data and signals that let agents work with you. APIs, feeds, and machine-readable product information reduce guesswork. If agents can confirm availability, pricing, or location from a trusted feed, your brand becomes a reliable component in the user’s automated flow. Pair that with clear permissions on how your data can be displayed or executed, so platforms have a reason to include you without fear of legal exposure.

Treat agent-mediated activity as its own marketing channel. Name it. Measure it. Fund it. You are early, so your metrics will change as you learn, but the act of measuring will force better questions about what visibility and conversion mean when browsers complete tasks for users. The first teams to formalize this channel will understand its economics long before competitors notice the traffic shift.

Finally, stay close to the platform evolution. Watch every release of OpenAI’s Atlas and Perplexity’s Comet. Track Google’s response as it blends Gemini deeper into Chrome and Search. The pace will feel familiar (like the late 2000s browser race), but the consequences will be larger. When the browser becomes an agent, it doesn’t just display the web; it intermediates it. Every business that relies on discovery, trust, or conversion will feel that change.

The Takeaway

Agentic browsers will not replace marketing, but they will reshape how attention, trust, and action flow online. The winners will be brands that think like system integrators (clear data, structured content, and dependable facts) because those are the materials agents build with. This is the early moment before the inflection point, the time to experiment while risk is low and visibility is still yours to claim.

History shows that when browsers evolve, the web follows. This time, the web won’t just render pages. It will think, decide, and act. Your job is to make sure that when it does, it acts in your favor.

Looking ahead, even a modest 10 to 15% adoption rate for agentic browsers within three years would represent one of the fastest paradigm shifts since Chrome’s launch. For marketers, that scale means the agent layer will become a measurable channel, and every optimization choice made now – how your data is structured, how your content is summarized, how trust is signaled – will compound its impact later.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Roman Samborskyi/Shutterstock

Anthropic Research Shows How LLMs Perceive Text via @sejournal, @martinibuster

Researchers from Anthropic investigated Claude 3.5 Haiku’s ability to decide when to break a line of text within a fixed width, a task that requires the model to track its position as it writes. The study yielded the surprising result that language models form internal patterns resembling the spatial awareness that humans use to track location in physical space.

Andreas Volpini tweeted about this paper and made an analogy to chunking content for AI consumption. In a broader sense, his comment works as a metaphor for how both writers and models navigate structure, finding coherence at the boundaries where one segment ends and another begins.

This research paper, however, is not about reading content but about generating text and identifying where to insert a line break in order to fit the text into an arbitrary fixed width. The purpose of doing that was to better understand what’s going on inside an LLM as it keeps track of text position, word choice, and line break boundaries while writing.

The researchers created an experimental task of generating text with a line break at a specific width. The purpose was to understand how Claude 3.5 Haiku decides on words to fit within a specified width and when to insert a line break, which required the model to track the current position within the line of text it is generating.

The experiment demonstrates how language models learn structure from patterns in text without explicit programming or supervision.

The Linebreaking Challenge

The linebreaking task requires the model to decide whether the next word will fit on the current line or if it must start a new one. To succeed, the model must learn the line width constraint (the rule that limits how many characters can fit on a line, like in physical space on a sheet of paper). To do this the LLM must track the number of characters written, compute how many remain, and decide whether the next word fits. The task demands reasoning, memory, and planning. The researchers used attribution graphs to visualize how the model coordinates these calculations, showing distinct internal features for the character count, the next word, and the moment a line break is required.

Continuous Counting

The researchers observed that Claude 3.5 Haiku represents line character counts not as counting step by step, but as a smooth geometric structure that behaves like a continuously curved surface, allowing the model to track position fluidly (on the fly) rather than counting symbol by symbol.

Something else that’s interesting is that they discovered the LLM had developed a boundary head (an “attention head”) that is responsible for detecting the line boundary. An attention mechanism weighs the importance of what is being considered (tokens). An attention head is a specialized component of the attention mechanism of an LLM. The boundary head, which is an attention head, specializes in the narrow task of detecting the end of line boundary.

The research paper states:

“One essential feature of the representation of line character counts is that the “boundary head” twists the representation, enabling each count to pair with a count slightly larger, indicating that the boundary is close. That is, there is a linear map QK which slides the character count curve along itself. Such an action is not admitted by generic high-curvature embeddings of the circle or the interval like the ones in the physical model we constructed. But it is present in both the manifold we observe in Haiku and, as we now show, in the Fourier construction. “

How Boundary Sensing Works

The researchers found that Claude 3.5 Haiku knows when a line of text is almost reaching the end by comparing two internal signals:

1. How many characters it has already generated, and
2. How long the line is supposed to be.

The aforementioned boundary attention heads decide which parts of the text to focus on. Some of these heads specialize in spotting when the line is about to reach its limit. They do this by slightly rotating or lining up the two internal signals (the character count and the maximum line width) so that when they nearly match, the model’s attention shifts toward inserting a line break.

The researchers explain:

“To detect an approaching line boundary, the model must compare two quantities: the current character count and the line width. We find attention heads whose QK matrix rotates one counting manifold to align it with the other at a specific offset, creating a large inner product when the difference of the counts falls within a target range. Multiple heads with different offsets work together to precisely estimate the characters remaining. “

Final Stage

At this stage of the experiment, the model has already determined how close it is to the line’s boundary and how long the next word will be. The last step is use that information.

Here’s how it’s explained:

“The final step of the linebreak task is to combine the estimate of the line boundary with the prediction of the next word to determine whether the next word will fit on the line, or if the line should be broken.”

The researchers found that certain internal features in the model activate when the next word would cause the line to exceed its limit, effectively serving as boundary detectors. When that happens, the model raises the chance of predicting a newline symbol and lowers the chance of predicting another word. Other features do the opposite: they activate when the word still fits, lowering the chance of inserting a line break.

Together, these two forces, one pushing for a line break and one holding it back, balance out to make the decision.

Model’s Can Have Visual Illusions?

The next part of the research is kind of incredible because they endeavored to test whether the model could be susceptible to visual illusions that would cause trip it up. They started with the idea of how humans can be tricked by visual illusions that present a false perspective that make lines of the same length appear to be different lengths, one shorter than the other.

Screenshot Of A Visual Illusion

The researchers inserted artificial tokens, such as “@@,” to see how they disrupted the model’s sense of position. These tests caused misalignments in the model’s internal patterns it uses to keep track of position, similar to visual illusions that trick human perception. This caused the model’s sense of line boundaries to shift, showing that its perception of structure depends on context and learned patterns. Even though LLMs don’t see, they experience distortions in their internal organization similar to how humans misjudge what they see by disrupting the relevant attention heads.

They explained:

“We find that it does modulate the predicted next token, disrupting the newline prediction! As predicted, the relevant heads get distracted: whereas with the original prompt, the heads attend from newline to newline, in the altered prompt, the heads also attend to the @@.”

They wondered if there was something special about the @@ characters or would any other random characters disrupt the model’s ability to successfully complete the task. So they ran a test with 180 different sequences and found that most of them did not disrupt the models ability to predict the line break point. They discovered that only a small group of characters that were code related were able to distract the relevant attention heads and disrupt the counting process.

LLMs Have Visual-Like Perception For Text

The study shows how text-based features evolve into smooth geometric systems inside a language model. It also shows that models don’t only process symbols, they create perception-based maps from them. This part, about perception, is to me what’s really interesting about the research. They keep circling back to analogies related to human perception and how those analogies keep fitting into what they see going on inside the LLM.

They write:

“Although we sometimes describe the early layers of language models as responsible for “detokenizing” the input, it is perhaps more evocative to think of this as perception. The beginning of the model is really responsible for seeing the input, and much of the early circuitry is in service of sensing or perceiving the text similar to how early layers in vision models implement low level perception.”

Then a little later they write:

“The geometric and algorithmic patterns we observe have suggestive parallels to perception in biological neural systems. …These features exhibit dilation—representing increasingly large character counts activating over increasingly large ranges—mirroring the dilation of number representations in biological brains. Moreover, the organization of the features on a low dimensional manifold is an instance of a common motif in biological cognition. While the analogies are not perfect, we suspect that there is still fruitful conceptual overlap from increased collaboration between neuroscience and interpretability.”

Implications For SEO?

Arthur C. Clarke wrote that advanced technology is indistinguishable from magic. I think that once you understand a technology it becomes more relatable and less like magic. Not all knowledge has a utilitarian use and I think understanding how an LLM perceives content is useful to the extent that it’s no longer magical. Will this research make you a better SEO? It deepens our understanding of how language models organize and interpret content structure, makes it more understandable and less like magic.

Read about the research here:

When Models Manipulate Manifolds: The Geometry of a Counting Task

Featured Image by Shutterstock/Krot_Studio