I recently came across an intriguing report by HUMAN Security revealing a seismic shift in internet traffic dynamics. Automated traffic is accelerating at a staggering rate, outpacing human traffic growth by eightfold. Machines aren’t just passively scrolling; they’re actively engaging online!
In 2025, automated traffic surged by 23.5% compared to the previous year, while human traffic increased by a mere 3.1%. This data, outlined in the State of AI Traffic report, paints a compelling picture of our digital evolution.
AI-driven traffic is spearheading this transformation, with its monthly volume skyrocketing by 187% year over year. Notably, AI agents and agentic browsers, such as OpenAI’s Atlas and Perplexity’s Comet, experienced an astonishing growth of nearly 8,000%!
As defined in the report, automated traffic encompasses all internet activity generated by software systems rather than humans. This includes classic automation like search engine crawlers and monitoring bots, as well as more sophisticated AI-driven traffic.
Matthew Prince, Cloudflare’s CEO, foresaw this trend, predicting that bots might overtake human web usage by 2027. A bold forecast, but one that seems increasingly plausible.
Why we care. The landscape of search is evolving beyond mere human interaction. AI agents now delve into discovery, comparisons, and transactions, dynamically influencing Google’s evolving results and other AI-powered interfaces.
The details. HUMAN categorizes AI-driven traffic into three main types:
1. Training crawlers, which still dominate AI traffic at 67.5%, though their lead is waning as scrapers and agents gain traction.
2. Real-time scrapers, which are essential for AI searches and live answer engines, boosted by nearly 600% in 2025.
3. Agentic AI systems, which autonomously execute tasks, are smaller in share but growing rapidly and proving to be highly disruptive.
AI agents behave more like users. These systems are becoming more sophisticated, engaging in navigation, logging in, and conducting transactions. In 2025:
– 77% of agentic activity was observed on product and search pages.
– Close to 9% involved account-level interactions.
– Over 2% reached checkout processes.
About the data. HUMAN examined over a quadrillion interactions from 2022 to 2025, using aggregated, anonymized data from its customers. The report classified AI-driven traffic into three categories using user-agent strings, infrastructure signals, and activity characteristics. However, self-reported bot identities may not fully capture AI-driven activity.
Bottom line. The digital world is shifting from being solely human-focused. Discovery is no longer restricted to search engines. Optimizing content now involves deciding which machines can access and act upon it.
I’ve recently come across news about a fascinating development from Google: the introduction of the Google-Agent user agent. It’s designed to signal when AI agents complete tasks on behalf of users, marking a significant step towards AI-driven web interactions. I’m eager to share what I’ve learned about this new feature and its implications.
What Happened: Google added Google-Agent to its collection of user-activated fetchers on March 20, and it’s currently rolling out gradually. This intrigued me because it means a novel way of tracking AI interactions is becoming available to us.
The Google-Agent user agent identifies requests made by AI programs running on Google’s infrastructure, which includes experimental tools like Project Mariner. It’s fascinating to see how advanced Google is getting in this space.
How It Works: Google-Agent appears in HTTP requests when an AI agent visits a site to complete tasks initiated by users. Imagine it like a helping hand behind the scenes, orchestrating internet tasks for us.
For example, Google-Agent could be used for browsing pages, evaluating content, or performing actions like submitting forms. This differs from traditional crawlers like Googlebot that operate continuously without user prompts. It’s exciting to think about how this technology could evolve further.
IP Ranges: Google has shared the IP ranges for its desktop agent, and notably for its mobile agent as well. This transparency is helpful as it allows us to better manage and identify website traffic.
Why We Care: With this insight, I can now distinguish between traditional crawl activity and visits spawned by users through AI agents using server logs. This capability will enable me to track agent-assisted conversions, understand emerging user behaviors, and prepare for what might be called ‘agentic search’.
What They’re Saying: According to Google’s announcement, “The Google-Agent user agent is rolling out over the next few weeks, and will be used by Google agents hosted on Google infrastructure to navigate the web and perform actions upon user request.” This statement makes me realize the potential impact on our digital interactions.
What to Watch: While early volumes of activity may be low, now is the ideal time to establish a baseline. Monitoring logs for Google-Agent activity ensures I stay informed, and I need to ensure that my CDN and WAF configurations aren’t unintentionally blocking these IP ranges.
Furthermore, it’s crucial for me to validate that key site actions, including forms and user flows, function smoothly for automated agents, ensuring an optimized experience for users.
I recently discovered that Google has enhanced its structured data support for forum and Q&A pages. This update introduces new properties that allow us to better signal reply threads, quoted content, and identify whether content is generated by AI or humans.
With these changes, which aim to boost Google’s accuracy in interpreting discussions and Q&A content, we can now ensure our content is represented more precisely.
What’s New. Google has updated its QAPage documentation to include commentCount and digitalSourceType. Moreover, the DiscussionForumPosting documentation now supports sharedContent alongside these new properties.
The Details. Using Q&A markup, I’m able to apply commentCount to questions, answers, and comments, showcasing the total number of comments even if they are not fully marked up. This total should align with answerCount + commentCount, representing all types of replies.
How It Works. The digitalSourceType property allows me to indicate whether content is produced by a model or simple automation. I can use TrainedAlgorithmicMediaDigitalSource for advanced outputs and AlgorithmicMediaDigitalSource for basic bots. If this property is left out, Google assumes the content is human-generated.
What’s New for Forums. The sharedContent property helps me to mark the primary item that’s being shared in a post. Google supports various content types like WebPage, ImageObject, and more, including quotes or reposts.
Why This Matters. This update provides me with greater control over how Google interprets community content, which is particularly important for sites rich in forums, support communities, UGC platforms, and Q&A sections. Google can now distinguish between answers and comments more effectively, tally partial threads across multiple pages, and recognize when a post primarily shares specific media types.
Documentation. The official documentation was updated on March 24, providing all the details I need to apply these new capabilities.
I recently delved into a fascinating study exploring how AI citations are significantly influenced by certain content formats. It turns out listicles, articles, and product pages are at the forefront, driving over 52% of mentions across various AI language models.
The research, conducted by Wix Studio AI Search Lab, analyzed a whopping 75,000 AI answers and more than a million citations across platforms like ChatGPT, Google AI Mode, and Perplexity. It’s an exciting revelation that showcases the power of content structure in digital landscapes.
The findings? Listicles claimed the top spot with 21.9% of citations, followed by articles at 16.7% and product pages at 13.7%. When combined, these formats make up a majority of the citations AI references.
What’s interesting is that articles tend to dominate when it comes to informational queries, being cited 2.7 times more than other formats. Meanwhile, listicles capture nearly 40% of commercial-intent citations, almost double compared to any other type.
The Why Behind Intent. It’s fascinating to see how query intent, rather than industry or AI model, is the strongest predictor of which content gets cited. This trend doesn’t shift much across different sectors, from SaaS to health industries.
Informational queries skew towards articles (45.5%) and listicles (21.7%), while commercial queries are dominated by listicles (40.9%). Interestingly, transactional and navigational queries favor product and category pages, with those two formats comprising about 40% of the citations combined.
The Impact for Us. This study is incredibly insightful, illustrating why aligning content types with user intent is more strategic than simply generating content. Articles serve to inform, listicles foster comparisons, and product pages drive conversions. Tailoring content to align with user goals might just be the key to snagging more AI citations and enhancing visibility.
Not all listicles perform equally. In professional services, third-party listicles account for 80.9% of citations, showing a preference for neutral editorial comparisons over branded lists by large language models.
Looking at Model Preferences. While all models have a penchant for listicles, their other preferences vary. ChatGPT leans heavily towards articles and informational content, Google AI Mode shows a balanced approach, and Perplexity stands out with 17% of its citations coming from discussions on platforms like Reddit and forums.
Industry-Specific Trends. Though preferences shifted slightly among industries, there are notable trends. SaaS and professional services veer towards listicles, health sectors favor authoritative articles, and ecommerce spreads its citations across listicles, articles, and category pages. Interestingly, home repair maintains an even distribution across different formats.
I’m intrigued to know more! The comprehensive research can be found here.
As I’ve navigated the evolving landscape of SEO over the years, one truth remains: our biggest challenges often come from within. We’re standing at the brink of 2026, and it’s becoming clear that our organization’s internal issues might be the most significant threat to SEO success.
In recent discussions, AI tools and their impact on visibility have taken center stage. Yet, the conversation often overlooks a crucial issue. The real danger lies within our organizations—fragmented data, unclear KPIs, and poor collaboration silently erode even the most well-crafted SEO strategies.
I want to share a few internal threats that we should start addressing now to ensure our SEO efforts remain effective.
Many of us lean heavily on AI for tasks ranging from brief creation to data analysis. While AI expedites these processes, it’s essential to avoid falling into the trap of a one-size-fits-all solution. AI can provide speed, but the key is still in our unique perspective—what differentiates our content from the rest?
Another concern is our fragmented data landscape. Despite advancements, we still struggle with incomplete information about our users’ journeys. Users engage with AI tools, forming product perceptions before reaching us, but we lack visibility into these early interactions.
This brings us to another challenge: setting appropriate KPIs. While traditional metrics like traffic remain relics of past success, we now need to focus on visibility, considering the evolving role of AI. We’re being pulled towards metrics that may not directly align with business outcomes.
Furthermore, our roles must adapt beyond mere SEO execution to influencing broader strategic goals. Holding ownership without execution leads to misalignment. Instead, our insight should guide multi-platform visibility strategies, while leadership assigns responsibility for execution.
I’ve noticed the absence of cross-team collaboration in leveraging AI visibility. If AI visibility isn’t a shared priority across teams, then executing a unified strategy becomes difficult. Our job includes rallying all teams around common goals.
As SEO shifts to adaptability in a fast-paced AI-influenced world, action becomes vital. We can’t afford to stall in strategizing without executing. As I’ve experienced, prompt action allows us to learn quickly and adapt strategies effectively.
Ultimately, strong collaboration defines successful SEO execution. As our field becomes integral to broader company capabilities, continued team effort ensures sustainable visibility.
I urge you to see beyond traditional SEO. Embrace it as a dynamic business capability. The organizations that recognize this will lead the way in efficient discovery and sustainable growth.
I recently had the opportunity to attend the Industrial Marketing Summit, where Rand Fishkin delivered a keynote highlighting our current “zero-click world”. His perspective resonated with me, emphasizing that while fewer users are visiting websites, their impact remains crucial.
Diving deeper, it’s evident that the structural dynamics of how information is assessed and trusted online have shifted profoundly. This change has led many to misunderstand the true value of websites today.
Despite the drop in clicks, websites still play a vital role. They are the bedrock of visibility and trustworthiness on the internet.
Why ‘zero-click’ discussions often lead to the wrong conclusion
There’s an undeniable trend: clicks are on the decline, and here’s why.
Search engines readily display answers directly on results pages.
Social media platforms have become discovery hubs, allowing users to explore without ever needing to leave.
AI assistants synthesize comprehensive responses from the web even before presenting a user with links.
The focus on zero-click results disrupts traditional metrics for measuring online visibility. For decades, traffic and click-through rates have been the cornerstones for evaluating search performance.
Yet, when answers are given directly by search results or AI systems, often outside our typical analytics frameworks, many assume websites are losing significance. This is far from the truth.
Websites still underpin the information ecosystem. Their role in shaping visibility is arguably becoming more significant, especially with AI and modern information systems relying heavily on widespread, consistent signals from multiple sources on the web.
Fishkin is right about the trend
Information today is discovered in various environments, including search results, social media, and AI interfaces, leading to a real fragmentation of how we consume content.
While these interactions might appear as lost website traffic, the true question is: where does the original information come from?
Although people consume information through expanding platforms, these systems fundamentally depend on credible, original knowledge sources.
Zero-click doesn’t mean zero influence
The critical takeaway is differentiating between traffic and information influence.
Traffic measures visits to your site.
Influence assesses if your information shaped the answers people received.
AI creates responses based on patterns from the web, and content creators who provide valuable information remain crucial in this ecosystem.
Even without direct clicks, reliable sources continue to influence the information pipeline, helping shape the responses generated by AI systems.
The role of ‘rented land’
In adapting to a zero-click landscape, the focus might shift towards platforms where brands lack control, such as social networks or other “rented lands”.
Visibility stems from both types of territory — owned and rented.
Owned land encompasses your controlled content like websites.
Rented land includes platforms that distribute your message but aren’t owned by you.
In an AI-driven discovery setting, both are valuable. Owned content serves as essential knowledge sources, while rented platforms amplify these insights.
Yet, authority primarily emerges from robust original content, typically housed on first-party sites, which remains pivotal in influencing AI systems.
Why AI often favors primary sources
Contrary to some beliefs, AI systems value primary sources more than aggregated content.
When AI generates answers, it frequently relies on sources with clear, expert explanations and well-reasoned content, mostly found in single-source publishing like legal blogs or technical documentation.
This move places emphasis on creating authoritative content, which can enhance your influence in an AI-led world even as click metrics may reduce.
The real shift you should understand
Websites are evolving beyond their historical role as mere traffic generators. They are now key players in the AI-mediated informational landscape as sources of knowledge and bastions of expertise.
The goal now is to ensure expertise is accessible and can be assimilated across various digital environments, be it search engines, AI responses, or social discussions.
In our zero-click world, influence takes root earlier, reinforcing the importance of creating valuable, knowledgeable content.
I’ve noticed that AI is drastically changing the landscape for marketing agencies, and it’s a pressure felt from both sides. Though we welcomed AI as a tool to enhance efficiency, it seems to be impacting our margins in unexpected ways.
In 2024, 44% of digital marketing agencies, including mine, identified AI as a potential threat. By 2025, this concern had increased to 53%, as highlighted in SparkToro’s survey of agency owners worldwide.
The real kicker? We aren’t just passive observers in the AI disruption; we’re actually participants. We’ve adopted AI to streamline tasks and reduce costs, attempting to boost our profitability. Meanwhile, our clients are following suit, using AI to cut budgets or opt to handle tasks internally.
This dual pressure has created a challenging environment for agencies like mine.
The Promise That Became a Problem
When advanced AI tools such as ChatGPT and Claude emerged, I initially saw them as opportunities. They offered ways to automate tedious tasks, ostensibly improving our efficiency and competitiveness.
Our equation appeared simple: automate more tasks with AI, reduce manpower, and profit from the savings. However, clients performed the same calculations and reached a different conclusion: why pay an agency when AI can produce satisfactory content, analyze campaigns, or generate ads on their own?
This shift prompted unwelcome questions about the value we provide.
Some services we once charged premium prices for are now being completed in-house or through automation tools. Al Sefati, CEO of Clarity Digital Agency, has frequently discussed the hurdles that boutique agencies face in this AI-driven market.
Earlier this year, I faced clients who “put marketing on pause,” despite good performance metrics. One manufacturing client even walked away from a contract due to tariff uncertainties. In tightening budget scenarios, where AI renders some marketing services commoditized, agencies like ours become easy targets for budget cuts.
The Margin Trap Nobody Talks About
We began using AI to do more with fewer team members, expecting to see higher profits. But our clients expect these savings to benefit them, not enhance our bottom line.
This has led to an unpleasant trend of shrinking retainers. SparkToro’s research indicates that sales cycles are becoming longer, with more agencies reporting delays in closing deals that extend from 7-8 weeks to over 12 weeks.
The reason? Potential clients are evaluating, “If AI makes this cheaper and faster, shouldn’t our rates be reduced as well?”
Even as efficiency through AI increases, client expectations haven’t decreased—they’ve grown. Agencies are now expected to demonstrate tangible results, link investments directly to revenue, and offer genuine ROI.
This presents a dilemma: adopt AI and risk downgrading our perceived service value, or resist AI changes and fall behind more adaptable competitors.
The Junior Talent Crisis Nobody’s Preparing For
One concerning insight from the report suggests that 66% of agency owners are worried about dwindling career opportunities for junior staff. Historically, agencies have relied on entry-level employees to perform routine tasks such as keyword research, content optimization, and campaign setup.
While not glamorous, these tasks are crucial stepping stones for junior marketers to develop skills and progress to strategy and client leadership roles. However, AI is rapidly taking over these process-oriented tasks.
This shift raises a vital question: how will we cultivate new talent if there’s no foundational work for them to learn from?
What AI Can’t Replace Yet
Despite the disruptions, some agencies are successfully navigating these changes. Larger agencies report healthier sales and stronger pipelines than smaller firms. This is partly due to their ability to weather economic changes and a focus on strategic offerings that AI cannot easily replicate.
Those of us thriving have stopped competing solely on execution. We now offer something AI can’t easily mimic: strategic insights, market experience, and storytelling that aligns with business outcomes.
“Clients desire teams that truly understand their industry,” notes Sefati.
Agencies that succeed are often those with deep expertise in specific verticals like B2B SaaS, financial services, healthcare, and ecommerce. This specialization allows us to maintain our value by offering nuanced insights and strategic thinking that AI struggles to deliver.
The Uncomfortable Truth About Commoditization
In the past, simply having the technical skills to launch campaigns gave agencies a competitive edge. But as AI and martech tools advance, more brands develop internal capabilities that rival what agencies offer.
This shift is reflected in data from SparkToro’s research, with only 14% of agencies claiming a “very healthy” pipeline, while the majority experience average or below-average pipelines.
Smaller agencies, especially those with 1-10 people, are feeling this pressure acutely. They often lack sales staff, forcing founders to juggle sales and client delivery roles, making it harder to compete when budgets shrink.
How Your Agency Can Escape the Squeeze
It’s crucial to focus on what AI can’t replicate and make strategic adjustments as client expectations rise and margins narrow.
Be Honest About What AI Has Commoditized
Embrace AI rather than shying away from it. Acknowledge what AI has commoditized and concentrate on areas it can’t;
If your agency still relies on AI-performed services such as basic content creation or standard reporting, it’s time to pivot. Focus on strategic, creative, or nuanced tasks that distinguish your agency from AI applications.
Lead with AI, Don’t Hide from It
Change the narrative around AI and lead with it in client discussions. Highlight the unique value add your agency provides beyond AI capabilities.
For instance, emphasize how only your team can fully understand a client’s market dynamics or interpret data insights contextually to improve strategic initiatives.
Rethink Pricing Models
Updating pricing strategies is essential. Outcome-based fees and performance partnerships could better align your agency’s incentives with client success, leveraging the efficiencies AI brings.
Rebuild the Talent Pipeline
Address the diminishing opportunities for junior staff by involving them in high-level strategic work alongside seasoned specialists. This approach will prepare the future frontline of agency talent as their role expands beyond AI-executed tasks.
The Old Agency Model Isn’t Coming Back
Over 64% of agencies are optimistic about revenue growth in the coming year, but this hinges on whether they innovate or wait for an outdated model to return—it won’t.
The squeeze is a lasting reality. The key to thriving is to reimagine what agencies offer and how we deliver it—making our roles indispensable, not replaceable.
Will your agency evolve to leverage AI’s capabilities and become irreplaceable, or will it be swept aside as clients discover they can handle tasks independently?
I’ve been eagerly following the latest developments from Google, and their new Ads DevCast is truly a groundbreaking resource for developers like me. This initiative offers technical insights into Google Ads and highlights how AI-driven changes are transforming ad APIs.
The new show is hosted bi-weekly by Cory Liseno, as part of the Google’s Advertising and Measurement Developer Relations team. Ads DevCast focuses on deep technical dives across key tools like Google Ads, Google Analytics, and Display & Video 360. It feels like a direct line to the experts who are constantly innovating in our field.
What’s interesting here is that Ads DevCast complements Ads Decoded, which is more about campaign strategy, hosted by Ginny Marvin. It’s specifically designed with us developers in mind, highlighting the need for a specialized approach to understanding these platforms.
The first episode, intriguingly titled “MCPs, Agents, and Ads. Oh My!”, delves into the “agentic shift” that Google is observing. With AI agents becoming the main users of ad APIs, this shift is something we’re all keenly interested in.
For those of us deeply involved with Google’s ad tools, Ads DevCast is an invaluable resource. It helps us stay ahead of technical evolutions, discover new capabilities quickly, and build efficient integrations in a landscape increasingly dominated by AI.
I see Google broadening the horizon from a niche “Ads Developer Community” to a wider “Ads Technical Community.” This change allows marketers to carry out technical tasks without needing exhaustive development cycles.
As a pilot project, Ads DevCast is still very much in development, and Google is actively seeking feedback from us to refine future episodes. It’s exciting to know we can influence its direction.
This initiative reinforces Google’s commitment to keeping us in the loop with their latest innovations, enabling us to adapt quickly and effectively in an AI-first world. Check out Ads DevCast if you haven’t already!
I’ve been intrigued by Google’s latest test in the Google Business Profile: AI-generated responses to customer reviews. This innovative tool offers businesses the ability to create suggested replies to reviews, which I can then review, tweak, and manually submit.
Why It Matters to Me. Engaging with customer reviews significantly impacts conversions and trust. However, I’m aware of the risks associated with generic AI replies, especially for negative reviews where sincerity is crucial. Personalized, quality responses are more influential than merely replying for the sake of it.
What I Saw. Here’s a sneak peek of how the feature appears:
The Details I’ve Discovered. It seems Google is conducting a limited roll-out of this ‘Reply to reviews with AI’ feature within the Google Business Profile.
It generates proposed responses to customer reviews.
I can review and modify these suggestions before submitting.
The availability fluctuates across different accounts and reviews.
The feature is spotted in the U.S., Brazil, and India, but not yet widely in Europe.
Initial Impressions. Some users, like me, noticed prompts targeting older, unanswered negative reviews.
In one test I observed, it’s possible to generate AI responses in bulk.
I’ve read mixed reports on automation—some claim bulk responses still need a review, while others experienced fully automated replies that require no edits.
How I First Learned About It. This feature caught my attention first through LinkedIn, thanks to Chandan Mishra, a freelance local SEO specialist, and it was further amplified by Darren Shaw, founder of Whitespark.
I recently heard Cloudflare CEO Matthew Prince predict a fascinating future where AI bots might outnumber us humans on the web by 2027. The surge of agent-driven browsing, paired with the rise of generative AI, could really shake things up online.
During his talk at SXSW, Prince warned us that bots are already transforming how we use and monetize the internet. This got me thinking about the big shift in search as more people rely on AI-generated answers instead of traditional clicks.
Why this matters to me. With the prospect of bots becoming the main users of the web, I’ll need to adapt my strategy. Ensuring AI systems can access and trust my content will be crucial for staying relevant.
Details from Prince. According to Prince, AI agents collect far more information than we do because of their unique browsing habits. While I might visit five sites for a purchase, an AI could browse thousands, generating significant traffic and load.
Prince also pointed out the rapid changes in the internet’s baseline.
He said that, for a long time, about 20% of web traffic was from bots, but by 2027, this could surpass human traffic.
This isn’t a sudden spike, like during COVID-19; it’s a steady increase with no signs of slowing down.
The broader implications. Prince compared this shift to other digital transformations, like mobile and social media. However, the difference here is profound: users may stop visiting websites directly, relying instead on AI interfaces for aggregated answers.
The traditional business model of attracting traffic and selling through ads is under threat. After all, bots don’t click on ads, and customers are more likely to trust an AI’s output without further clicks.
AI sandboxes. I found Prince’s vision of “AI sandboxes” particularly intriguing. These temporary environments for AI agents could appear and disappear millions of times per second, impacting how computing works behind the scenes.
Such changes will undoubtedly put sustained pressure on our internet infrastructure as traffic continues to grow.
Business ramifications. Companies are already debating how to adapt to AI’s influence, and there’s no clear consensus yet. Prince highlighted how the nature of bots might sever the direct relationship between businesses and their customers, as bots don’t prioritize brands.
For content creators like me. AI can be both a challenge and an opportunity. It might reduce direct traffic, challenging ad-based models, but it also creates demand for unique, original data, which AI companies may pay for.
Local media could thrive by licensing specific content to AI companies, potentially earning more than through digital ads.
For small businesses. Prince put it wisely: AI agents prioritize price, quality, and efficiency over brand loyalty. This means traditional trust shortcuts might not hold any longer, driving towards relentless aggregation.
Future considerations. The next era hinges on finding ways to balance control and compensation for content producers and providers. In Prince’s words, “There has to be some exchange of value.”
The fundamental question remains unanswered: what will be the future business model of the internet?
