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SerpApi asks court to throw out Reddit scraping complaint The legal landscape surrounding data scraping, intellectual property, and search engine accessibility is currently undergoing a massive transformation. At the heart of this shift is a high-stakes legal battle between Reddit, the self-proclaimed front page of the internet, and SerpApi, a company that provides tools to scrape search engine results pages (SERPs). SerpApi has officially moved to have Reddit’s lawsuit dismissed, a move that could set a major precedent for how data is handled in the age of generative AI and automated data collection. The motion to dismiss follows an amended complaint filed by Reddit in February, which sought to tighten the legal noose around SerpApi and several other defendants. However, SerpApi argues that Reddit’s claims are not only factually thin but represent a dangerous attempt to expand platform power over content that Reddit does not technically own and data that is fundamentally public. The Core of the Dispute: Ownership and the User Agreement One of the primary pillars of SerpApi’s defense centers on the question of who actually owns the content posted on Reddit. In a blog post addressing the legal action, SerpApi CEO Julien Khaleghy pointed out a significant irony in Reddit’s legal strategy. According to Reddit’s own User Agreement, the individuals who post content—the users—retain ownership of their contributions. While Reddit holds a non-exclusive license to host, display, and distribute that content, it does not possess the full copyright ownership required to sue third parties for copyright infringement in the manner they are attempting. SerpApi argues that Reddit is attempting to use copyright law as a blunt instrument to control information it does not own. If the court agrees with SerpApi, it could undermine Reddit’s entire legal standing in the case. Under U.S. copyright law, to bring a successful infringement claim, the plaintiff typically must prove they own the valid copyright to the material in question. By admitting in their terms of service that users retain ownership, Reddit may have created a legal barrier for itself that is difficult to bypass. The Nature of Search Snippets Another critical aspect of the defense involves the nature of the data being “scraped.” Reddit’s complaint highlights the use of snippets—short fragments of text, dates, addresses, and usernames—that appear in search results. SerpApi contends that these fragments are not copyrightable. Under the “de minimis” doctrine and the factual nature of such data, short phrases and metadata generally do not meet the threshold of original creative work required for copyright protection. Furthermore, SerpApi emphasizes that they are not scraping Reddit directly. Instead, they are accessing Google Search pages. This distinction is vital to their legal strategy. When a user searches Google, Google displays snippets of various websites, including Reddit. SerpApi provides a service that allows users to see what Google is showing. Therefore, SerpApi argues they are acting as a middleman for public search data rather than a pirate of Reddit’s private database. The DMCA Controversy: What Constitutes Circumvention? Reddit’s legal team has invoked the Digital Millennium Copyright Act (DMCA), alleging that SerpApi violated the law by circumventing technical protections Reddit put in place to prevent scraping. The DMCA was originally designed to prevent the hacking of digital rights management (DRM) software, such as the encryption on a DVD or a streaming service. Khaleghy and the SerpApi legal team dispute this application of the DMCA. They argue that accessing a public webpage that is freely available to any human with a web browser does not constitute “circumvention.” SerpApi does not break encryption, bypass login credentials, or hack into secure servers. They simply retrieve the same search results that are visible to anyone who enters a query into Google. SerpApi’s motion suggests that Reddit is trying to redefine “technical protections” to include any measure—such as bot detection or IP blocking—that is intended to stop automated access. If the court sides with Reddit, it could mean that simply finding a way around a basic bot-blocker could be treated as a federal crime under the DMCA, a prospect that has the broader tech community and the SEO industry deeply concerned. Contextualizing the Conflict: A Timeline of Legal Escalation The battle between Reddit and SerpApi did not happen in a vacuum. It is part of a broader series of legal actions Reddit has taken as it seeks to monetize its data in the wake of the AI boom. As large language models (LLMs) like GPT-4 and Gemini require massive amounts of human conversation data for training, Reddit’s archives have become incredibly valuable. This has led to a flurry of litigation and public disputes: In October 2022, Reddit filed its initial lawsuit against SerpApi, alongside other entities like Perplexity AI, Oxylabs, and AWMProxy. Reddit alleged that these companies were scraping its content through Google Search and reusing it at scale, often to power AI responses that compete with Reddit’s own platform traffic. A key piece of evidence cited by Reddit was a “trap” post—a piece of content visible only to Google’s crawler and not to human users. When this trap post appeared in responses generated by Perplexity, Reddit claimed it was “smoking gun” evidence of unauthorized scraping. Shortly after the initial filing, SerpApi fired back in late October, calling Reddit’s allegations inflammatory. They defended their right to access public search data, framing the issue as one of information freedom versus corporate gatekeeping. The situation became even more complex in December 2023, when Google itself sued SerpApi. Google’s lawsuit alleged that SerpApi was bypassing its bot protections and scraping licensed search features, such as “People Also Ask” and “Knowledge Graph” boxes. This put SerpApi in the crosshairs of two of the largest data-driven companies in the world simultaneously. By February 2023, SerpApi asked the court to dismiss Google’s lawsuit, using a similar argument to the one they are now using against Reddit: that Google is misusing the DMCA to restrict access to what is essentially public information. The current motion against Reddit is the latest move in this

The landscape of search engine marketing is undergoing its most significant transformation since the inception of Google Ads. For decades, the industry operated on a foundational principle: the keyword. Digital marketers spent countless hours building exhaustive lists of exact, phrase, and broad match terms, trying to predict every possible permutation a user might type into a search bar. However, the paradigm is shifting. We are entering an era defined not by strings of text, but by intent, audience signals, and machine learning. Today, AI-powered campaigns—specifically Performance Max (PMax) and the newer AI Max features—are redefining the rules of engagement. These tools leverage automation to identify opportunities that human managers might overlook, operating at a scale and speed that manual optimization cannot match. But as the role of the keyword diminishes, the role of the strategic marketer becomes more critical than ever. Success in this new environment requires a sophisticated understanding of how to guide the machine, rather than simply letting it run on autopilot. Industry experts like Nikki Kuhlman (VP of Search at Jumpfly), Brad Geddes (Founder of Adalysis), and Christine Zirnheld (Director of Lead Gen at Cypress North) have highlighted that the modern PPC professional must strike a delicate balance between automation and control. Mastering AI-driven campaigns is no longer about “setting and forgetting”; it is about providing the right data and constraints to ensure the AI delivers high-value results. Understanding AI Max for Search: A New Evolution One of the most frequent points of confusion for modern advertisers is the distinction between different AI-driven features. AI Max for Search is not a standalone campaign type like Performance Max. Instead, it is a one-click opt-in setting found within existing Search campaigns. It functions as an evolution of traditional search tactics, utilizing your landing pages and site assets to expand keyword reach in a manner similar to Dynamic Search Ads (DSA) or broad match, but with a higher degree of personalization. From Static Ad Groups to Dynamic Relevance In the traditional Google Ads setup, relevance was dictated by the ad group structure. If you bid on a keyword like “skincare for dry sensitive skin,” you would typically direct that user to a specific moisturizer page with pre-written ad copy. The problem arose when a user’s query didn’t perfectly align with your keyword list, or when Google’s matching algorithms triggered an ad group that wasn’t the best fit. In the current ecosystem, a specific ad group no longer provides a 100% guarantee that a specific keyword will trigger a specific ad. AI Max for Search solves this by dynamically generating ad headlines based on the actual search query. It analyzes the content of your landing page to ensure the messaging is hyper-relevant to the user’s immediate need. This creates a seamless bridge between the searcher’s intent and the final destination, often resulting in higher click-through rates (CTR) and better engagement. Unlocking the Power of Blog Content for Conversions Historically, PPC managers have been hesitant to use blog posts as landing pages. Traditional Dynamic Search Ads campaigns often excluded blogs because they were perceived as “top-of-funnel” content that didn’t drive direct sales. AI Max for Search is changing this perspective. By leveraging machine learning to identify high-intent segments within informational content, AI Max can effectively serve blog posts as landing pages that actually convert. The success here lies in the “guide” approach. When a blog post provides valuable information and then steers the reader toward a specific product or service, it builds trust. AI Max creates headlines that are often longer and more compelling than what humans can draft within the strict limits of traditional Responsive Search Ads (RSAs), leading to a superior user experience. Best Practices for Implementing AI Max for Search To succeed with AI Max, you cannot treat it as a universal solution for every campaign. It requires a tiered approach based on the data maturity of your account. Strategies for Success (The “Do” List) Leverage Existing Data: Only apply AI Max to campaigns that have a solid history of performance and conversion data. The AI needs a baseline to understand what a “good” lead looks like. The 50/50 Experiment: Never switch a successful campaign entirely to AI Max without testing. Use Google’s experiment framework to run a split test, allowing you to compare the AI-driven version against your manual baseline. Focus on Brand Inclusions: Use AI Max on brand campaigns where you have strong name recognition. This ensures the AI stays within the guardrails of your brand identity. Boost Under-Paced Campaigns: If you have campaigns that are consistently failing to spend their daily budget despite having room to grow, AI Max can help find the “incremental” volume needed to scale. Active Exclusion Management: Just because the AI is driving the ship doesn’t mean you stop looking at the map. Regularly review search query reports and landing page performance. Use URL exclusions to prevent traffic from hitting “About Us” or “Terms of Service” pages. Pitfalls to Avoid (The “Don’t” List) Avoid Fresh Launches: Do not use AI Max on brand-new campaigns without any data. Without historical signals, the AI may spend budget on irrelevant traffic while it tries to “learn” your business. Respect Budget Constraints: If a campaign is already hitting its budget cap every day, adding AI Max will likely increase your Cost Per Acquisition (CPA) without adding meaningful volume. AI Max is an expansion tool, not a budget-saving tool. Don’t Half-Measure: If you turn off both URL expansion and text customization, you are essentially neutering the AI. In those cases, you are better off sticking with traditional broad match and smart bidding. The Match Type Puzzle: What 16,000 Campaigns Reveal One of the most debated topics in digital marketing is the relevance of match types in an AI-driven world. A massive study analyzing over 16,000 campaigns has provided concrete data on how Exact, Phrase, and Broad match perform under different bidding strategies. The results challenge many long-held industry assumptions. Match Type Definitions in the Age of

The digital landscape is currently undergoing its most significant transformation since the invention of the graphical web browser. For decades, search engine optimization (SEO) has been built on a relatively stable foundation: users enter keywords, search engines crawl and index pages, and a list of blue links directs traffic to websites. This “Search Monolith” is now crumbling. As Large Language Models (LLMs) and Google’s AI Overviews become the primary interface for information retrieval, the old rules of engagement are being rewritten in real-time. Recent industry analysis, including a notable perspective from the Harvard Business Review, suggests that we are entering a “zero-click” era where user journeys are being collapsed. Instead of a multi-touch process involving several website visits, an AI model synthesizes a complete answer in seconds. While many marketers recognize this shift, there is a dangerous tendency to fall back on surface-level tactics that provide a false sense of security. To build lasting visibility in an AI-driven search world, brands must look past the “flock tactics” of today and focus on deep, structural optimizations that influence how machines think and reason. The Evolution of the Zero-Click Environment In the traditional SEO model, the goal was to capture “real estate” on the search engine results page (SERP). If you ranked in the top three positions, you were almost guaranteed a specific percentage of traffic. AI Overviews and LLM-based assistants like ChatGPT, Claude, and Perplexity have fundamentally disrupted this flow. They are not just search engines; they are synthesis engines. They ingest vast amounts of data to provide a direct answer, often removing the need for the user to ever click through to a source website. This collapse of the customer journey means that your brand’s “first impression” is no longer your homepage or a landing page. Instead, the first impression is the way an algorithm describes your brand, your products, or your expertise. When the AI becomes the gatekeeper, your marketing strategy must shift from optimizing for clicks to optimizing for “presence” and “authority” within the model’s latent space. If the model doesn’t know you, or if it hallucinates about you, your brand effectively ceases to exist in that user journey. The Problem with Flock Tactics As marketers scramble to respond to AI, many are gravitating toward what can be described as “flock tactics.” These are strategies that are easy to explain at the executive level and simple to implement, but they offer very little long-term competitive advantage because they are easily replicated by every competitor in the space. The Misunderstanding of Schema Markup Schema.org markup has long been a staple of technical SEO, providing search engines with structured data about products, reviews, and events. While Microsoft has confirmed that Bing Copilot utilizes schema to understand data, and Google certainly uses it for its Knowledge Graph, relying on schema as a primary AI optimization strategy is a mistake. Schema is “table stakes.” Once every major player in your industry has implemented product and organization schema, the competitive advantage disappears. Furthermore, LLMs are increasingly adept at processing unstructured data. They don’t necessarily need a JSON-LD script to understand that a page is a product review; they can infer it from the natural language. The real challenge isn’t just providing structured data on your own site, but ensuring your brand’s data is present in the external systems that LLMs prioritize, such as Wikidata or high-authority industry databases. Shallow E-E-A-T and Authorship Signals Google’s emphasis on Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) is more important than ever, but the way many brands approach it is purely cosmetic. Adding a headshot, a short bio, and a list of credentials to a blog post is a surface-level signal. In an era where AI can generate fake personas and credentials in seconds, these signals carry diminishing weight unless they are backed by real-world data. True authority in the AI era is built through an “expert entity” strategy. Models look for evidence of an author’s existence across the broader web. Does this person speak at recognized conferences? Is their work cited in academic journals or by major news outlets? Do they contribute to industry standards or open-source projects? A bio on your own website is a claim; a citation from a third-party authority is proof. LLMs prioritize the latter when determining which voices to amplify in their responses. The Trap of Vanity Concepts A common suggestion for building AI visibility is to create branded frameworks or “vanity concepts”—for example, inventing a proprietary name for a common process and hoping the AI will associate that name with your brand. While this sounds like a smart branding play, it rarely works in practice unless the concept gains genuine organic traction outside of your own marketing channels. If your “proprietary framework” is only mentioned on your own website, an LLM is likely to view it as marketing collateral rather than established knowledge. For a concept to influence an AI’s world model, it needs to be discussed, debated, and adopted by other entities. Without third-party validation, these vanity concepts remain invisible to the models, contributing nothing to your search visibility. Shifting from Strings to Things: Entity-First Optimization The most profound shift in SEO is the move from “strings” (keywords) to “things” (entities). Traditional SEO was obsessed with keyword density and matching. AI-driven search is obsessed with relationships between entities. An entity is a well-defined object or concept—a person, a place, a brand, or a specific technology. To build lasting visibility, you must manage your brand as an entity within a wider knowledge graph. This involves more than just content creation; it requires data engineering. You need to ensure that the relationships between your brand and other established entities are clear and verifiable. For example, if your company is a leader in “Sustainable Cloud Computing,” the AI should see clear connections between your brand and environmental standards, specific cloud technologies, and recognized industry leaders in sustainability. LLMs don’t just “read” your website; they look for consensus. If Google, Wikipedia, industry journals,

Only 15% of pages retrieved by ChatGPT appear in final answers: Report The landscape of search engine optimization is undergoing a seismic shift. For decades, the goal for digital publishers and SEO professionals was simple: rank on the first page of Google. However, with the rise of AI-driven search tools like ChatGPT, the metrics for success are changing. It is no longer enough to simply be “found” by an algorithm; your content must now survive a rigorous selection process internal to the AI itself. A comprehensive new study by AirOps has revealed a startling reality for content creators: ChatGPT retrieves far more information than it actually shares with the user. According to the report, a staggering 85% of the webpages that ChatGPT crawls and “reads” during the research phase of a query never make it into the final response. Only 15% of retrieved pages earn a coveted citation. This finding suggests that we are entering an era where “discovery” is merely the first hurdle. The real challenge lies in “selection”—the process by which an AI decides which specific sources are authoritative, relevant, and concise enough to be presented as a reference. For those in tech and gaming publishing, where accuracy and up-to-the-minute data are paramount, understanding this 15% threshold is critical to maintaining visibility. The Gap Between Retrieval and Citation To understand why so much content is being left on the cutting room floor, we must first understand how ChatGPT handles a user prompt. Unlike a traditional search engine that presents a list of links and leaves the filtering to the human user, ChatGPT acts as a synthesis engine. It performs what is known as Retrieval-Augmented Generation (RAG). In the RAG process, the AI identifies a broad set of potential sources that might contain the answer to a user’s question. This is the retrieval phase. However, once the information is gathered, the AI’s internal logic filters these sources. It looks for the most direct answers, the most reputable data, and the pages that best align with the specific intent of the prompt. The AirOps analysis, which looked at 548,534 pages across 15,000 prompts, proves that this filter is incredibly narrow. The fact that 85% of pages are discarded means that many websites are successfully optimized for discovery but are failing at the synthesis stage. They are visible to the AI’s “spider,” but they aren’t providing the level of utility required to be cited as a primary source. This shifts the focus of SEO from keyword density and backlink profiles toward deep relevance and information density. Analysis by Query Type: Where Do Citations Land? Not all searches are created equal. The AirOps report highlights that the likelihood of being cited fluctuates significantly based on the intent of the user’s query. This suggests that the AI’s “threshold for quality” changes depending on what the user is trying to accomplish. Product Discovery Queries: 18.3% Citation Rate Product discovery searches—such as “What are the best mechanical keyboards for gaming in 2025?”—saw the highest citation rate at 18.3%. This is likely because product recommendations require a diverse set of viewpoints and specifications. When ChatGPT provides a list of recommendations, it often pulls from multiple review sites to ensure a balanced perspective, giving more creators a chance to be featured. How-To and Informational Queries: 16.9% Citation Rate How-to queries, such as “How to optimize Windows 11 for high FPS,” yielded a 16.9% citation rate. In these instances, the AI prioritizes clarity and step-by-step accuracy. Pages that are structured with clear headings, lists, and direct instructions are more likely to be selected from the retrieved pool. Validation Searches: 11.3% Citation Rate The lowest citation rate occurred during “validation” searches, where users are looking for a specific fact or seeking to confirm a piece of information (e.g., “Does the RTX 4090 support DisplayPort 2.1?”). At just 11.3%, this category is the most difficult to break into. For these queries, ChatGPT often finds the answer in a few highly authoritative sources and discards the rest. If five sites say the same thing, the AI will likely only cite the one it deems most “trusted” or the one it crawled first. The Phenomenon of “Fan-Out” Queries One of the most enlightening aspects of the AirOps report is the concept of “Fan-out” searches. Most users assume that when they type a prompt into ChatGPT, the AI performs a single search. In reality, ChatGPT frequently expands a single user prompt into multiple internal searches to gather a more comprehensive data set. This creates what researchers call a “second citation surface.” The data shows that 89.6% of prompts triggered two or more follow-up searches. In the study’s dataset, 15,000 initial prompts were expanded into over 43,233 total queries. This is an incredible opportunity for SEOs who understand how to target long-tail, specific information. Crucially, 32.9% of all cited pages appeared only in these fan-out results. They were not found during the initial, broad search but were discovered when the AI dug deeper into specific sub-topics. For example, a prompt about “upcoming RPG games” might fan out into a specific search for “Avowed release date rumors.” Perhaps most importantly, 95% of these fan-out queries had zero traditional search volume on platforms like Google. This means that AI is searching for information that humans aren’t necessarily typing into a search bar. They are looking for the “connective tissue” of a topic. To win in this environment, content creators must cover niche details and secondary questions that surround a main topic, rather than just targeting high-volume keywords. The Correlation Between Google Rankings and AI Citations For those wondering if traditional SEO is dead, the AirOps report provides a definitive answer: No. In fact, ranking well on Google is one of the strongest predictors of being cited by ChatGPT. The study found that 55.8% of cited pages were ranked within the top 20 of Google’s search results. The advantage of being in the top spot is even more pronounced. Pages holding the Number 1 position on

The Death of the Vanity Metric: Why Traffic No Longer Equals Success For years, the standard enterprise SEO reporting call has followed a predictable, and ultimately broken, script. Agencies or internal teams present a slide deck filled with upward-trending line graphs showing organic sessions, impressions, and “keyword reach.” They celebrate a 15% increase in top-of-funnel traffic while the Chief Marketing Officer (CMO) looks at a sales pipeline that remains stubbornly flat. In the current economic climate, this disconnect is no longer sustainable. Marketing budgets are under unprecedented scrutiny, and every dollar must justify its existence through clear, attributable ROI. The hard truth is that optimizing for raw traffic volume is a legacy mindset—one that hides mediocre commercial performance behind a veil of vanity metrics. The new mandate for the enterprise CMO is to transition away from being a “traffic buyer” and toward becoming an “authority builder.” This requires building an acquisition engine that influences buyers and protects the profit and loss (P&L) statement long before a transaction even occurs. To survive as a marketing leader today, you must ruthlessly challenge your teams to stop reporting on operational output and start delivering hard financial accountability. The New Path to Purchase: Why Traffic is Bleeding Your Budget The traditional marketing funnel is being disrupted by a fundamental shift in how consumers and B2B decision-makers find information. Chasing top-of-funnel informational traffic is increasingly becoming a trap. When you pay for content that attracts users looking for general information—users who have no intention of buying—you are effectively subsidizing vanity metrics that do nothing for your bottom line. This shift is driven by the rise of Large Language Models (LLMs) and AI-driven search engines. Buyers now use tools like ChatGPT, Claude, and Perplexity to conduct deep, synthesized research before they ever land on a traditional search engine results page (SERP). By the time a user types a transactional query into Google, they have often already narrowed their choices down to two or three brands. If your brand is not the cited authority during that initial AI-driven research phase, you are invisible by the time the buyer reaches the transactional layer. You aren’t just losing traffic; you’re losing the “mindshare” that dictates the final purchase. The 7.48% Reality: The Power of the Educated Buyer The data reveals a staggering contrast in traffic quality when comparing traditional organic search to AI-driven discovery. Across enterprise client bases, traditional organic search typically converts at a rate of roughly 2.75%. In contrast, traffic originating from AI search citations converts at an average of 7.48%. Why is there such a massive disparity? It comes down to the “trust proxy.” LLMs function as the ultimate validator for today’s consumers. When an AI tool synthesizes dozens of expert reviews, whitepapers, and technical forums to recommend a specific enterprise solution, the user views that recommendation as an objective consensus. By the time a user clicks on an AI citation and arrives at your site, they are no longer “browsing.” They have been armed with data, comparisons, and third-party validation. They are an educated buyer prepared to transact. For a CMO, this means that one visitor from an AI citation is worth nearly three visitors from a standard organic link. From Found to Cited: Architecting the Default Recommendation Capturing that 7.48% conversion rate requires a total evolution of your digital asset strategy. In the legacy SEO model, the goal was to “rank” among a list of blue links. In the new model, the goal is to be “cited” as the definitive option by the algorithms that guide human decision-making. Winning the AI consensus requires you to treat your content creation as structured capital management. You are no longer just “writing blogs”; you are building data-rich assets designed for machine extraction. The Old Way vs. The New Way Consider the difference in approach for an enterprise logistics company: The Old Way: The team spends weeks publishing a 2,000-word blog post on “Top Supply Chain Trends for 2024.” It generates 5,000 monthly visitors, most of whom read the first three paragraphs and bounce. It adds zero value to the pipeline because it is too broad and lacks proprietary depth. The New Way: The company builds a Generative Engine Optimization (GEO) hub. This includes a dedicated supply chain cost calculator with proprietary data tables, expert author schema tagging the lead engineers, and strict “answer-first” formatting. LLMs require verifiable facts and consensus to generate confident answers. By structuring your digital assets with proprietary data and verifiable entities, you become the “default recommendation.” You may only get 500 visitors to this calculator instead of 5,000 to the blog post, but those 500 visitors are high-intent leads who are using your tool to justify a massive enterprise purchase. Strategic ROI: Using Citation Authority to Reduce Ad Spend One of the most significant failures in modern enterprise marketing is the siloed nature of SEO and Paid Media. SEO is often viewed as “free” traffic, while Paid is viewed as “guaranteed” traffic. This division creates massive financial inefficiencies. A sophisticated CMO must treat organic citation authority as a strategic financial lever to reduce overall Customer Acquisition Cost (CAC). When your organic assets dominate the AI Overview or the top of the SERP, your paid team has the opportunity to pull back on defensive ad spend. The IF/THEN Logic of Integrated Search To maximize ROI, your search strategy should follow a strict logical framework: IF your brand is established as the default AI recommendation for a high-cost commercial category, THEN your paid team must aggressively reduce defensive brand bidding. There is no reason to pay for a click on your own brand name if you already own the primary AI citation and the top organic result. This slashes the overall Cost Per Acquisition (CPA). IF paid search data identifies a highly profitable long-tail query with high conversion rates, THEN the SEO team must prioritize building a structured, data-heavy asset to capture that demand organically. This ensures that you don’t have to keep paying

The landscape of digital advertising is undergoing a seismic shift. As we look toward the horizon of 2026, the traditional methods of auditing Google Search Ads are no longer just becoming dated—they are becoming obsolete. The emergence of sophisticated AI-driven campaign types, the push for massive campaign consolidation, and the transition from manual controls to “indirect” signals have fundamentally changed the relationship between advertisers and the Google Ads platform. Brandon Ervin, Director of Product Management for Google Search Ads, recently appeared on Google’s Ads Decoded podcast to discuss these very shifts. The conversation touched on the evolution of “AI Max” (the next iteration of Performance Max), the necessity of campaign consolidation, and the future of advertiser control. While Ervin presented a vision of a platform that is more intuitive and powerful than ever, there remains a significant disconnect between Google’s product vision and the boots-on-the-ground reality experienced by media buyers and performance marketers. To succeed in 2026, an audit cannot simply be a checklist of settings. It must be an economic evaluation of how value is being distributed across your account. If you are still auditing your accounts using 2020 frameworks, you are likely missing the “value redistribution” that is quietly eroding your profit margins. The Paradox of “New” Controls: Innovation or Restoration? Google has introduced several updates recently that are aimed at giving advertisers more “control” over automated systems. On the surface, these look like major wins for the community. These updates include: Brand exclusions within Performance Max and Demand Gen campaigns. The ability to exclude site visitors and existing customers from PMax. Improved network-level reporting within bundled campaigns. Enhanced visibility into search terms. Brand and geographic controls at the ad group level within AI Max. Semantic modeling that reduces the “learning period” risk during campaign consolidation. While these are indeed helpful tools, a rigorous 2026 audit must view them through a critical lens. Many of these “innovations” are actually just the restoration of features that were standard before the aggressive push toward automation began. For example, the ability to separate brand from non-brand traffic was a fundamental setting for a decade. When Google removed that clarity in early iterations of PMax, it created a transparency gap. Reintroducing it years later is not necessarily a step forward; it is a restoration of a baseline that should never have been removed. An effective audit today must determine whether you are utilizing these tools to reclaim lost control or if you are still operating in the “black box” era of 2022-2024. Establishing the 2026 Table Stakes Before diving into the high-level economic audit, every account must have its fundamentals in order. In 2026, these are considered “table stakes.” If your account fails these basics, the more advanced AI models will have no foundation to build upon. The Foundational Checklist Your audit should first verify that the following are active and optimized: Full Ad Extensions: Sitelinks, callouts, structured snippets, images, and call extensions must be fully populated to maximize the “real estate” your ad occupies on the SERP. Intentional Automated Bidding: While manual bidding is nearly extinct, automated bidding must be governed by intentional targets (tCPA or tROAS) that align with actual business margins. Negative Keyword Hygiene: Even with broad match dominance, negative keyword lists remain your primary tool for preventing budget waste. Creative Relevance: Ads must be dynamically relevant to the queries they serve. This means using RSA (Responsive Search Ads) effectively with high-quality assets. Asset Auditing: Regularly review automatically created assets. Google’s AI is getting better at generating headlines and descriptions, but it can still produce brand-unsafe or inaccurate copy. Channel Exclusion: For most pure search campaigns, cutting Search Partners and Display expansion remains a best practice to ensure your budget stays focused on high-intent searchers. The Shift to Downstream Signals The most important part of the 2026 foundation is your data feedback loop. You must move beyond surface-level conversion tracking (like “Form Fills” or “Add to Carts”). To feed the Google AI what it actually needs, you must import offline conversion data. This includes Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQLs), actual revenue, and even Customer Lifetime Value (CLV). If the algorithm only sees “leads” but doesn’t see which leads turn into “revenue,” it will optimize for the cheapest, lowest-quality leads it can find. Core Pillar 1: Signal Architecture In the *Ads Decoded* podcast, Brandon Ervin argued that “control still exists, it just looks different.” This is a crucial takeaway for any 2026 audit. We have moved from “Direct Controls” (exact match keywords, device modifiers, manual bids) to “Indirect Controls” (data quality, signal density, and signal selectivity). In the past, you told Google exactly what to do. Today, you tell Google what you value, and the AI decides how to get it. Therefore, your audit must focus on the architecture of those signals. Quality vs. Surface Conversions Are you passing revenue and pipeline data back to Google? If you are a B2B company and you aren’t passing “Closed-Won” data back into the system, your AI Max campaigns are essentially flying blind. An audit should map out exactly which conversion actions are being used for “Primary” optimization and whether those actions correlate with actual profit. Density and Learning AI models require a certain volume of data to function. If your campaigns are too fragmented (the “anti-consolidation” approach), you won’t have enough conversion density for the model to learn. However, if you consolidate too much, you lose the ability to differentiate between high-value and low-value segments. The 2026 audit must find the “Goldilocks zone” of campaign structure: enough data to fuel the AI, but enough segmentation to maintain business logic. Selectivity Are you passing everything to Google indiscriminately? A high-performing account in 2026 is selective. This might mean only passing net-new customer data or weighting high-value customers more heavily than one-time buyers. You influence the algorithm by being picky about the data you feed it. Core Pillar 2: The Incrementality Challenge Google’s optimization engine is designed to maximize *reported*

The landscape of digital advertising is on the precipice of its most significant transformation since the invention of the search engine. For decades, Google has dominated the global market by perfecting the art of placing the right ad in front of the right person at the moment of intent. However, as the world pivots toward generative AI, the traditional “ten blue links” model is being challenged by conversational interfaces like Gemini. For months, the industry questioned how Google would monetize this new frontier without alienating its massive user base. Now, we have a clearer answer: the door is officially open. Recent statements from high-ranking Google executives signal a pivot in the company’s long-term strategy for Gemini. While earlier rhetoric suggested a cautious, almost hands-off approach to advertising within the AI chatbot, the narrative has shifted toward integration. This evolution marks a critical moment for marketers, tech enthusiasts, and the broader digital economy, as the world’s most powerful advertising engine prepares to merge with its most advanced artificial intelligence. The Shift from “No Plans” to “When, Not If” To understand the current trajectory, we must look back at the beginning of 2024. In January, during the World Economic Forum in Davos, Google DeepMind CEO Demis Hassabis provided a relatively firm stance on the matter. At the time, Hassabis told reporters that Google had no immediate plans to introduce advertising into the Gemini experience. This was seen as a way to prioritize user trust and refine the core technology before cluttering the interface with commercial content. However, the corporate stance has matured. In a recent interview, Nick Fox, Google’s Senior Vice President of Search, signaled a notable departure from that hardline denial. Fox indicated that while Google is still being deliberate, they are “not ruling out” the inclusion of ads within Gemini. This shift suggests that the conversation at Google has moved from the philosophical question of “should we?” to the practical question of “how and when?” For a company that generated over $400 billion in revenue in 2025, the majority of which stems from its advertising ecosystem, the eventual monetization of its flagship AI product was perhaps inevitable. The “prioritization question,” as Fox frames it, implies that the infrastructure for AI-based advertising is already being conceptualized behind closed doors. AI Mode: The Testing Ground for Future Ad Formats Google is not diving headfirst into Gemini ads without data. Instead, the company is utilizing its “AI Mode”—the Gemini-powered features integrated directly into Google Search—as a sophisticated sandbox. By testing ad formats within AI-generated search summaries (often referred to as AI Overviews), Google can observe user behavior and ad performance in a controlled environment before migrating those learnings to the standalone Gemini app. The current strategy in AI Mode focuses on three primary pillars: 1. Strict Separation and Clear Labeling One of the primary concerns with AI-generated content is the potential for “hallucinations” or biased information. To maintain credibility, Google ensures that ads are kept distinct from organic AI responses. These placements are clearly labeled as “Sponsored” or “Ads,” adhering to long-standing transparency standards. This distinction is vital for maintaining user trust in a conversational environment where the line between a recommendation and an advertisement can easily blur. 2. Extreme Relevance or Nothing In a traditional search result page, showing a “close enough” ad might still yield a click. In a conversational AI experience, an irrelevant ad feels intrusive and disruptive. Google has stated that it only serves ads in AI Mode when they are highly relevant to the specific query. If the AI determines that no commercial partner perfectly fits the user’s intent, it simply doesn’t show an ad. This “quality over quantity” approach is designed to prevent the AI from feeling like a telemarketing tool. 3. Leveraging Two Decades of Search Expertise Google isn’t starting from scratch. The company is drawing on more than 20 years of data regarding user intent, click-through rates, and auction dynamics. This historical data allows Google to predict with high accuracy which commercial interactions will be helpful to a user in a conversational flow. By the time ads officially land in the Gemini app, they will likely be powered by the most sophisticated relevance engine ever built. Monetization Pressures: Google vs. OpenAI The timing of Google’s shift in rhetoric is not accidental. The competitive landscape for generative AI is heating up, and the pressure to monetize is mounting across the industry. However, Google’s position is vastly different from that of its primary rival, OpenAI. OpenAI, despite its massive valuation and cultural impact, is under significant pressure to scale its revenue. Recent reports suggest the company is aiming to more than double its $30 billion revenue target. To achieve this, OpenAI has already begun testing ads in the free tier of ChatGPT. For OpenAI, advertising is a necessary survival mechanism to offset the astronomical costs of training and running large language models (LLMs). Google, by contrast, has the “luxury of patience.” With a revenue stream exceeding $400 billion, Google can afford to lose money on Gemini in the short term to ensure the user experience is perfected. This allows Google to watch OpenAI’s missteps and refine their own ad delivery system. But while Google has the luxury of time, they cannot wait forever. As users shift their search habits from standard queries to AI conversations, Google must ensure its revenue model shifts along with them. The “Personal Intelligence” Factor: The Holy Grail of Targeting One of the most intriguing aspects of Nick Fox’s recent insights involves “Personal Intelligence.” This refers to Gemini’s ability to integrate with a user’s personal Google ecosystem, including Gmail, Google Photos, and Google Calendar. By understanding a user’s schedule, their upcoming travel plans, and their personal preferences, Gemini becomes more than a chatbot—it becomes a digital assistant. Fox described this level of personalization as the “holy grail” for Search. If this personal data layer eventually informs the broader search and ad experience, the implications for advertisers are staggering. Imagine an AI that

The Evolution of Search: Why Traditional Link Building Is Falling Behind For nearly two decades, the backbone of Search Engine Optimization (SEO) was a relatively straightforward formula: create content, identify keywords, and acquire as many backlinks as possible. In the early days, quantity often outweighed quality. As Google’s algorithms matured, the focus shifted toward relevance and authority. However, we are currently witnessing the most significant shift in the history of the internet: the transition from traditional search engines to AI-driven discovery engines. The rise of Generative AI, Large Language Models (LLMs), and AI-integrated search results—such as Google’s AI Overviews and ChatGPT Search—has fundamentally altered how information is indexed and presented. In this new landscape, the “old” methods of link building, such as directory submissions, low-tier guest posting, and transactional link exchanges, are not just losing effectiveness; they may actually be hindering a brand’s ability to appear in AI-generated answers. To thrive in this environment, marketers and SEO professionals must pivot toward a strategy that prioritizes brand legitimacy and digital PR. The goal is no longer just to “get a link,” but to earn a place within the knowledge graphs that power modern AI. This requires a sophisticated approach to top-tier media placements that verify a brand’s authority to both human readers and machine learning algorithms. Understanding the Shift from Links to Entities To understand why traditional link building is struggling, we must understand how AI search differs from traditional Boolean or keyword-based search. Traditional search engines looked for “strings”—specific sequences of characters. If a website had the right keywords and enough backlinks with matching anchor text, it ranked well. AI search engines, however, look for “entities.” An entity is a well-defined concept or object, such as a person, a place, or a brand. AI models use a process called “semantic mapping” to understand the relationship between these entities. When an AI provides a response to a user query, it isn’t just looking for a page with high PageRank; it is looking for the most “trusted” source of information regarding a specific entity. In this context, a link from a high-authority, top-tier media outlet acts as a massive signal of legitimacy. It tells the AI that your brand is a recognized authority within its niche. This is why a single mention in a publication like The Wall Street Journal or Wired is now worth more than a thousand links from obscure, mid-tier blogs. The former builds entity authority; the latter merely inflates a metric that AI is increasingly trained to ignore. The Decline of the Transactional Link Building Model The “old” link-building model was largely transactional. SEOs would reach out to webmasters, often offering content or payment in exchange for a link. This led to a cluttered ecosystem of “guest post sites” that exist solely to sell links. Google has become incredibly adept at identifying these patterns, often devaluing these links entirely or, in worse cases, penalizing the sites involved. AI search takes this a step further. Because LLMs are trained on massive datasets of human language, they can distinguish between natural editorial citations and forced, artificial link placements. AI models prioritize “consensus.” If multiple high-authority news organizations and industry journals are talking about a brand in a specific context, the AI accepts that brand as a factual authority. Transactional links from low-quality sources do not contribute to this consensus; they are filtered out as noise. Why Top-Tier Media Placements Are the New Gold Standard Earning placements in top-tier media has always been a goal for public relations professionals, but it is now a critical requirement for SEO. These placements serve three primary functions in the age of AI search: 1. Validating E-E-A-T Signals Google’s focus on Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) is more prominent than ever. When an expert from your company is quoted in a major publication, or when your brand’s original research is cited by a reputable news desk, it provides the ultimate validation of E-E-A-T. AI models use these citations to verify that the information you provide is accurate and backed by real-world authority. 2. Feeding the AI Training Sets LLMs are trained on the “Common Crawl” and other massive repositories of internet data. However, not all data is weighted equally. Developers of AI models prioritize high-quality, edited, and fact-checked content. By securing placements in top-tier media, you ensure that your brand’s name and expertise are included in the high-quality datasets that future AI models will use to “learn” about your industry. 3. Driving Referral Traffic and Brand Awareness While the SEO benefits are paramount, we cannot overlook the traditional value of media placements. Top-tier outlets have massive, engaged audiences. A single well-placed article can drive thousands of qualified leads to your site. In an era where AI might provide the answer directly on the search results page (zero-click searches), having a strong brand that people recognize and search for by name is a vital safeguard. Strategies for Earning Top-Tier Media Placements Moving away from old link building requires a new toolkit. You cannot “buy” your way into the New York Times; you have to earn your way in. This process, often called Digital PR, involves several key strategies. Original Data and Proprietary Research Journalists are always looking for new, interesting data to support their stories. If your company has access to unique data points, you can package this into a research report or a white paper. By providing journalists with “the first look” at a new trend or statistic, you provide immense value. When they write about your findings, they will almost certainly cite your brand as the source, creating a high-authority link and a strong entity signal for AI. The “Expert Source” Methodology News moves fast. When a major event happens in your industry, journalists need expert commentary immediately. By positioning your C-suite executives or lead researchers as “on-call” experts, you can secure mentions in breaking news stories. Platforms like Connectively (formerly HARO) or Featured.com are useful, but direct relationship building with journalists

The Changing Landscape of Google Search The digital publishing world is currently navigating one of the most significant shifts in the history of the open web. For decades, the relationship between Google and publishers was relatively symbiotic: publishers provided the content, and Google provided the audience through organic search results. However, the introduction and aggressive expansion of Google’s AI Overviews (AIO) has fundamentally altered this dynamic. According to a comprehensive new report from Define Media Group, the impact is no longer theoretical—it is measurable, and for many, it is stark. The report reveals that organic search clicks have plummeted by 42% since the broader rollout of AI-generated summaries. This decline represents a massive redistribution of traffic that threatens traditional SEO strategies while simultaneously opening new, albeit different, doors for growth. As Google transforms from a “search engine” that directs users to websites into an “answer engine” that provides information directly on the results page, the industry is witnessing a pivot toward real-time reporting and feed-based discovery. A Deep Dive into the Numbers: The 42% Decline To understand the gravity of these findings, it is essential to look at the dataset provided by Define Media Group. The analysis drew from Google Search Console data across a diverse portfolio of 64 high-traffic websites. This wasn’t a small sample size; the baseline traffic for these sites was substantial, providing a clear window into how user behavior has changed since AI became the centerpiece of the search experience. From the first quarter of 2023 through the first quarter of 2024, organic search traffic for this portfolio was stable, averaging approximately 1.7 billion clicks per quarter. This period serves as the “pre-AI” baseline. The disruption began almost immediately after the initial launch of AI Overviews. Upon the first implementation, search traffic saw an immediate 16% dip. Unlike previous algorithm updates where traffic might fluctuate and then stabilize, this traffic never recovered to its original levels. The situation intensified in May 2025, when Google significantly expanded the footprint of AI Overviews. This expansion meant that more queries across a wider range of categories were being met with an AI-generated summary at the top of the page. By the fourth quarter of 2025, the cumulative loss was staggering: organic search clicks had dropped by a total of 42% compared to the pre-AI baseline. This trend suggests that as Google refines its AI, the “zero-click” search—where a user finds their answer without ever leaving Google—is becoming the new standard for informational queries. The Polarization of Content: Evergreen vs. Breaking News While the overall 42% drop is alarming, the report highlights that the pain is not being felt equally across all types of content. The data reveals a sharp polarization between “evergreen” or informational content and breaking news. Evergreen content, which includes how-to guides, definitions, and general information, has historically been the bread and butter of long-term SEO. Unfortunately, this is exactly the type of content that AI Overviews are best at summarizing. When a user asks “How to change a tire” or “What is the capital of Kazakhstan,” the AI can provide a concise, accurate answer sourced from the web, removing the need for the user to click on a specific article. Consequently, publishers who rely heavily on “how-to” and general knowledge traffic are seeing their search referrals evaporate. Conversely, the report found a remarkable surge in traffic for breaking news. From November 2024 through early 2026, breaking news traffic grew by 103%. This suggests that while Google is comfortable using AI to answer static questions, it is still leaning heavily on traditional publishers to provide real-time updates on developing stories. For the news industry, the “Top Stories” carousel remains a vital lifeline, often appearing in place of, or more prominently than, AI summaries during major events. Google Discover: The New Lifeblood of Web Traffic As traditional web search traffic declines, a new hero has emerged for publishers: Google Discover. The Define Media Group report indicates that Discover traffic grew by 30% across their portfolio during the same period that search clicks were falling. Perhaps the most significant finding in the report is that, for the first time, Discover and traditional web search now drive roughly equal amounts of traffic for many major publishers. Google Discover operates differently than Search. While Search is intent-based—meaning a user is looking for something specific—Discover is interest-based. It pushes content to users based on their browsing history and preferences through a feed on mobile devices. This “push” model is proving to be more resilient to AI disruption than the “pull” model of traditional search. The growth in Discover traffic appears to be a deliberate part of Google’s ecosystem shift. As the company uses AI to satisfy specific queries, it is using Discover to keep users engaged with a curated stream of fresh content. For publishers, this means that “optimizing for Discover”—which involves high-quality imagery, engaging headlines, and timely topics—is now just as important, if not more so, than traditional keyword-based SEO. Why AI Overviews Shy Away from Real-Time News One of the most intriguing aspects of the report is the low frequency with which AI Overviews appear for news-related queries. Data from Ahrefs cited in the report shows that AI Overviews appeared for only about 15% of news queries. This is nearly three times less often than in categories like health, science, or technology, where the information is often more factual and less time-sensitive. There are several logical reasons why Google is exercising caution with AI in the news space: 1. The Risk of Hallucination Generative AI models are prone to “hallucinations”—confidently stating facts that are incorrect. In the context of breaking news, where details change by the minute, the risk of providing a false summary is high. Google likely views the “Top Stories” carousel as a safer alternative, as it attributes information directly to trusted news brands rather than generating its own interpretation. 2. High Accuracy Stakes For topics like international conflicts, political developments, or public safety, the stakes for

The Evolving Landscape of B2B Decision-Making The rapid integration of artificial intelligence into the business world has promised a revolution in efficiency, data processing, and customer interaction. From automated lead nurturing to 24/7 customer support chatbots, AI is everywhere. However, a recent report focusing on B2B decision-makers has revealed a significant disconnect between the availability of AI tools and the trust buyers place in them. According to the findings, B2B buyers trust peer recommendations nearly twice as much as they trust information provided by AI chatbots. This revelation highlights a critical human element that remains immovable despite the technological shift: the value of lived experience. While AI can process billions of data points in seconds, it currently lacks the professional credibility and accountability that comes from a colleague or industry peer who has navigated similar challenges. This shift in trust dynamics is reshaping how companies approach their marketing and sales funnels. It suggests that while AI is an excellent tool for productivity, it is not yet viewed as a reliable source for high-stakes decision-making. For marketers and business leaders, understanding this gap is essential for building a strategy that resonates with modern buyers who are increasingly skeptical of automated narratives. The Power of Peer Recommendations: Why Human Connection Wins In the B2B sector, the stakes are high. Purchases often involve six-figure budgets, multi-year contracts, and significant organizational changes. When a decision-maker chooses a new software platform or a professional service provider, their professional reputation is on the line. The report indicates that peer recommendations are the gold standard for trust. This is likely due to several key factors that AI cannot currently replicate: Accountability and Risk Mitigation When a peer recommends a product, they are staking their own credibility on that recommendation. If a colleague tells you that a specific CRM transformed their sales pipeline, you trust that information because they have no ulterior motive other than professional courtesy. In contrast, an AI chatbot is perceived as a tool programmed by the vendor, inherently carrying a bias toward the product it represents. Shared Context and Industry Nuance Peers understand the specific “pain points” of an industry. They know the regulatory hurdles, the integration headaches, and the cultural shifts required to implement new technology. A chatbot might provide a technical summary of a product’s features, but a peer can explain how those features actually perform during a high-stress quarterly audit or a massive data migration. The Rise of “Dark Social” Much of this peer-to-peer influence happens in what marketers call “Dark Social”—private Slack channels, closed LinkedIn groups, and face-to-face networking events. These are environments where AI cannot reach and where traditional tracking metrics fail. The report’s findings confirm that these private conversations carry more weight than any public-facing AI interface or marketing collateral. The Skepticism Surrounding AI Chatbots While AI chatbots have become more sophisticated with the rise of Large Language Models (LLMs), the B2B community remains wary. The report’s finding that trust in AI is significantly lower than trust in peers points to several systemic issues within the current state of AI technology. The Problem of Hallucinations and Accuracy One of the biggest hurdles for AI in B2B sales is the risk of “hallucinations”—instances where the AI confidently provides incorrect information. In a B2B context, where technical specifications and contract terms must be precise, a single piece of misinformation can derail a deal or lead to a costly mistake. Buyers are aware of these limitations and are therefore hesitant to rely on AI for critical research. The Lack of Transparency B2B buyers often want to know the “why” behind a recommendation. AI chatbots, particularly those built on proprietary models, often function as a “black box.” It is difficult for a user to trace how the AI reached a specific conclusion or whether the information is being filtered to favor the vendor’s most profitable packages. Without this transparency, trust remains elusive. The “Human Touch” in Complex Negotiations The B2B buying journey is rarely linear. It involves negotiation, customization, and relationship building. Chatbots excel at answering frequently asked questions, but they struggle with the nuances of a complex negotiation. Buyers feel more comfortable talking to someone who can empathize with their specific situation, a trait that AI, by its very nature, can only simulate. The Decline of the Traditional White Paper Perhaps the most surprising finding in the report is the ranking of white papers. Once considered the cornerstone of B2B content marketing, white papers now rank last for perceived value among decision-makers. This marks a significant shift in how professionals consume information and signifies the end of an era for “gated content” as a primary lead generation tool. Information Overload and Time Constraints Modern B2B buyers are busier than ever. The traditional 20-page white paper, filled with dense jargon and lengthy case studies, is often seen as a chore rather than a resource. Buyers are moving toward “snackable” content—short videos, interactive tools, and concise executive summaries that provide immediate value without requiring a significant time investment. Perceived Bias and Sales Intent Over the years, the quality of white papers has become inconsistent. Many have transitioned from objective, research-based documents into glorified sales brochures. Buyers have become savvy to this; they see a white paper as a biased document designed to push them toward a specific solution rather than an educational tool. This skepticism has driven the perceived value of the format to an all-time low. The Shift to Real-Time Data In a fast-moving tech economy, a white paper published six months ago might already be obsolete. Buyers are looking for real-time insights, live webinars, and dynamic data visualizations. Static PDFs simply cannot compete with the immediacy of social media discussions or live-updated industry benchmarks. Strategies for B2B Marketers in a Peer-Driven Market The report’s findings serve as a wake-up call for B2B organizations. If buyers trust peers over AI and value white papers the least, marketers must pivot their strategies to focus on community, advocacy, and authentic engagement. Prioritizing Customer