AI SEO

The landscape of search is poised to undergo transformative changes in the coming years, driven by advances in AI and shifting user behaviors. Generative Engine Optimization (GEO) will need to evolve in step with these trends. This chapter explores five key frontiers shaping the future of search and what they mean for online marketing professionals: personal AI assistants, multimodal search and answers, voice search 2.0, evolving AI algorithms and transparency, and the enduring role of human creativity . Each section delves into emerging developments and provides guidance on how to adapt content and SEO strategy accordingly. Personal AI Assistants: The Personalized Search Revolution One of the most significant shifts on the horizon is the rise of personal AI assistants – AI agents tailored to individual users, drawing on personal data and preferences in addition to general web knowledge. These AI “co-pilots” are changing how people find information and make decisions, effectively becoming intermediaries between users and the web. In this future, consumers may rely less on traditional search engines and more on AI-driven agents like smart assistants, chatbots, or custom AI models that act on their behalf ( [1] ). AI Agents in Everyday Life: Changing Search Behavior AI assistants are already gaining traction in daily life. A 2024 McKinsey study found 41% of Gen Z consumers rely on AI-driven assistants for shopping and task management ( [1] ) – from getting recommendations on what to buy to managing calendars. This trend spans demographics and is expected to grow. These assistants (e.g. OpenAI’s ChatGPT, Google’s Bard, Amazon’s Alexa, Apple’s Siri, or newcomer apps like Inflection’s Pi) are increasingly capable of complex reasoning and personalization. They don’t just fetch information; they execute tasks and influence decisions autonomously ( [2] ). For example, instead of a user performing multiple Google searches to plan a trip, a personal AI agent could handle the entire process: considering the user’s past travel preferences, budget from personal finance apps, and live flight data to propose a tailored itinerary. In effect, the AI agent bypasses traditional search engines, drawing on content it has pre-ingested or fetched via APIs to provide answers or actions ( [3] ). This has profound implications: content that isn’t accessible or trusted by these AI agents will be invisible in this new channel of discovery. Real-world example: Microsoft’s Copilot in Windows 11 and Office 365 is an early example of a broad personal AI assistant for both consumers and enterprise workers. By late 2024, nearly 70% of Fortune 500 companies had adopted Microsoft 365 Copilot to assist employees with tasks like drafting emails, summarizing documents, or answering questions from internal data ( [4] ). These AI copilots combine a large language model (OpenAI GPT-4 in this case) with user-specific context (emails, files, calendar) to deliver highly relevant answers. Similarly, Google Assistant with Bard/Gemini integration is enabling more personalized help on mobile devices. Google’s Assistant can now use its Gemini AI model to understand images and context from your screen, and even draw on your personal Gmail or documents (if permissions allow) to answer queries ( [5] ) ( [6] ). For instance, you might ask on your phone: “Remind me what my flight details are and what’s a good restaurant near my hotel? ” – a Gemini-powered Assistant could parse your email for the flight info and cross-reference Google Maps for restaurant reviews, all in one conversational reply. Such scenarios illustrate how personalized AI search is becoming: the AI taps both private data (user’s emails, calendar, past behavior) and public web data to craft an answer. From a marketer’s perspective, this means the traditional funnel of users searching on Google and clicking a link is being disrupted. The AI assistant might never display a list of blue links ; instead, it gives a spoken or written answer synthesized from multiple sources. As one AI SEO expert put it, by 2025 failing to be present in the “trusted dataset” of AI assistants will be akin to not ranking in Google today ( [7] ). GEO strategy, therefore, must consider “AI visibility” – ensuring your content is among the sources these AI agents draw upon. Integration of Personal Data and Enterprise AI Personal AI assistants aren’t just consumer tools; businesses are adopting them as well. Enterprise AI assistants (like IBM Watson Assistant or custom chatbots fine-tuned on company data) are helping employees and customers get information more efficiently. Microsoft reports that generative AI adoption reached 75% of companies in 2024, with many seeing significant ROI from AI assistants handling routine queries ( [8] ). For example, a large consulting firm used a custom AI agent to automate parts of client onboarding, cutting lead time by 90% ( [9] ). These enterprise agents often combine internal knowledge bases with web information. For marketers, this opens a new front: B2B GEO. If companies use AI assistants to find vendors or solutions (instead of manually searching), you need to ensure your thought leadership content, case studies, and product info are accessible to those AI systems. This could mean providing structured data feeds or APIs to enterprise clients, or simply continuing to produce high-authority content that an AI agent evaluating options would deem credible. Moreover, personal assistants in home and life – from smart home hubs (Alexa, Google Nest Hub) to car assistants (Tesla’s planned integration of xAI’s Grok chatbot into vehicles) – will draw on web content for answers. Amazon’s revamp of Alexa with generative AI is instructive: in late 2024 Amazon announced “ Alexa Next Gen,” powered by Anthropic’s Claude model, to handle more complex questions ( [10] ) ( [11] ). This new Alexa can converse more naturally and provide detailed answers (it even has a paid tier for advanced AI capabilities) – essentially becoming a ChatGPT-like search agent you talk to. Amazon hopes this will finally get users to engage in shopping and recommendations via Alexa, something that earlier voice search hadn’t achieved ( [12] ). Early signals suggest voice commerce may indeed pick up: voice assistant users are 33% more likely to have made an online purchase in the past week than the average consumer ( [13] ) ( [14] ). As these assistants get better at transactions, optimizing your product listings for voice/AIs or integrating with assistant platforms (e.g. ensuring your retail site’s products are indexed in Amazon’s or Google’s shopping graphs that Alexa/Assistant pull from) will be critical. Optimizing Content for Personal AI Assistants How do we adapt GEO strategies for … Read more

In the era of Generative Engine Optimization (GEO), marketers need to rethink how they define and measure success. Traditional SEO metrics like search rankings, click-through rates, and organic traffic volume are no longer the sole barometers of performance. Instead, success in GEO is about being part of the answer – ensuring that AI-driven search tools reference your brand and content in their responses. This article explores the new metrics and tools for measuring GEO success, from tracking how often your site is cited in AI answers (your “reference rate” ) to monitoring brand sentiment in AI-generated content, and how to adapt your KPIs and processes accordingly. From Clicks to “References” For decades, SEO success was gauged by where your site ranked on a search engine results page and how many users clicked through. In the GEO landscape, visibility is measured in references, not just rankings. Large language model (LLM) search interfaces (like chat-based answers and AI summaries) don’t display a list of blue links for users to click – they synthesize information into a direct answer. That means your brand’s presence depends on whether the AI includes you in its answer, rather than how high you appear on a SERP. In short, “reference rate” – how often your content or brand is cited as a source in AI-generated answers – becomes a key metric of success ( [1] ). As one industry expert put it, “It’s no longer just about click-through rates, it’s about reference rates” ( [1] ). GEO entails optimizing for what the model chooses to mention, not just optimizing for a position in traditional search results ( [2] ). This shift fundamentally changes how we define online visibility. In the past, a marketer might celebrate a #1 Google ranking that drove thousands of clicks. Now, imagine an AI assistant (like Google’s Search Generative Experience or ChatGPT) answers a user’s question with a paragraph that mentions your brand or quotes your content. Even if the user never clicks a link, your brand has achieved visibility within the answer itself. These “zero-click” interactions are increasingly common. In fact, even before generative AI, over half of Google searches ended without any click to a website as users found answers directly on the results page ( [3] ). Generative AI has amplified this trend by providing rich answers that often preempt the need for a click. Thus, getting mentioned by the AI – as a cited source, a footnote, or even an uncited brand name in the narrative – can be as valuable as getting a click. One emerging metric in GEO is the reference rate, which measures the frequency of your brand/content being referenced by AI. This could take several forms:  Explicit citations: e.g. your webpage is cited as a source with a hyperlink. Google’s AI Overviews (formerly SGE) often include a handful of source links in an “According to [Site]…” format or a “Learn more from…” section. Bing Chat likewise footnotes its statements with numbered citations linking to websites. If your page appears in those cited sources, that counts as a reference.  Implicit mentions: Sometimes an AI model will mention a brand or product in its answer without a formal citation. For instance, a ChatGPT response (with default settings) might say “Brand X is known for affordable pricing” based on its training data, even if it doesn’t link to Brand X’s site. Such an uncited mention still indicates the model has included your brand in the answer, which contributes to brand awareness (and can prompt the user to search you out separately).  Suggested content and follow-ups: Some AI search experiences suggest related topics or follow-up questions. If your brand appears in those, it’s another form of reference. For example, if a user asks an AI, “What’s the best project management software?” and the AI’s answer lists a few options including your product (with or without a link), that inclusion is a win for GEO. In GEO, we are essentially shifting from “Did the user click my link?” to “Did the AI mention my brand or content?”. An immediate implication is that brand awareness via AI becomes critical. If an AI frequently references your site as an authority, it not only drives any direct AI referral traffic, but also boosts your brand’s mindshare. Users may see your name in an AI summary and later navigate to your site or Google your brand (this is analogous to how appearing in a featured snippet could raise awareness even among non-clickers). A recent analysis by Ahrefs underscores this dynamic: in a study of 3,000 sites, 63% of websites had at least one visit from an AI source over a short period, yet on average only 0.17% of total traffic came directly from AI chatbots ( [7] ) ( [8] ). The takeaway is that while AI-driven visits are currently a small fraction of traffic, a majority of brands are already being touched by AI in some way – often via mentions that may not immediately show up as a click. In other words, you might be getting “reference traffic” (influence and visibility) even when you’re not getting click traffic. Over 60% of websites in a 2025 study received at least some traffic from AI chatbots. However, the average share of total traffic from AI was only ~0.17%, indicating that AI-driven visibility often doesn’t translate into large click volumes – many user queries are answered without a click ( [7] ) ( [8] ). This underscores the importance of measuring references (mentions in AI answers) in addition to traditional click metrics. Because of this shift, SEO practitioners are now talking about metrics like “AI Reference Rate” or “AI Share of Voice.” These terms describe the proportion of relevant AI answers that include your brand. For example, if out of 100 popular questions in your industry, your site is referenced in 10 of the AI-generated answers, you have a 10% share of voice in that AI domain. Some early case studies illustrate why this matters: in one instance, a B2C eyewear brand (Warby Parker) was found to command a 29% share of voice on ChatGPT for a set of eyewear-related queries, outperforming competitors like Zenni and Eyebuydirect ( [9] ). However, the same brand had a weaker presence in Google’s generative search results (Gemini AI), highlighting that reference rates can vary significantly by platform ( [10] ). GEO success means … Read more

Understanding User Prompts in the Age of AI Search The way people search for information is shifting from typing keywords into a browser to asking full-fledged questions in conversational AI tools. A few years ago, a typical query might have been “best running shoes 2022” —terse and keyword-based. Today, users are more likely to ask an AI assistant something like: “Which running shoes are best for long-distance runners with flat feet under $150?” This represents a fundamental change in search behavior. People treat AI assistants like ChatGPT, Google’s Bard (powered by Gemini ), or Bing Chat as if they were human experts, phrasing queries in natural language and expecting direct, well-reasoned answers ( [1] ) ( [2] ). The implication for marketers is clear: Optimizing solely for terse keywords is no longer enough. We must understand and anticipate the actual questions our audience might pose to an AI engine about our industry, products, or services. This evolution is happening rapidly. By mid-2024, roughly one in ten U.S. internet users were already turning to generative AI first for online search ( [3] ). Global studies show ChatGPT and Google’s Gemini (the model underpinning Bard and Search Generative Experience) dominate this emerging AI search market with 78% of traffic ( [4] ). Bing Chat and newcomers like Perplexity.ai constitute much of the rest ( [5] ). In practical terms, that means tens of millions of people are asking ChatGPT or similar tools for advice and answers instead of (or before) using Google. A 2024 Statista survey estimated 13 million Americans were using generative AI as their primary search tool in 2023, and projections suggest this could soar to over 90 million by 2027 ( [6] ) ( [7] ). Gartner analysts even predict traditional search volume may drop 25% by 2026, with organic traffic potentially decreasing by more than 50% as consumers embrace AI-powered search ( [6] ). This trend is illustrated by the AI platform traffic share in 2024, where ChatGPT and Google’s AI account for the lion’s share. These numbers underscore a pivotal point: AI is becoming the new gatekeeper between customers and content ( [8] ) ( [9] ). Users are delighted by getting a single, synthesized answer without browsing multiple sites ( [10] ). But for marketers, it’s disconcerting – the AI now decides which brands or pages “deserve” a mention in its answer ( [8] ). To thrive in this environment, we must research how our target audience phrases questions to AI and ensure our content aligns with those queries. Start by considering the intents and language your customers use. For example, a consumer might ask: “ How can I fix acne breakouts without harsh chemicals? ” if you’re in the skincare industry, or “ What’s the best CRM software for a mid-size e-commerce business? ” if you offer B2B software. Notice how these resemble the natural, specific questions one would ask a knowledgeable person, rather than stilted keyword strings. Brainstorm (or better, research ) the common questions in your niche:  Product comparison prompts: “Which [product] is best for [specific need] ?” (e.g., “Which laptop is best for graphic design under $1000?” ).  Problem-solution prompts: “How do I solve [problem] without [undesirable solution] ?” (e.g., “How to unclog a drain without harsh chemicals?” ).  Alternative prompts: “What can I use instead of X ?” (e.g., “What are good alternatives to Photoshop for beginners?” ).  Best-of lists and tips: “What are the top N [items] for [goal] ?” (e.g., “What are the top 5 project management tips for remote teams?” ). One way to gather these prompts is to consult your existing data and customer interactions. Talk to sales and support teams about the questions they hear most. Analyze community forums, Reddit, Quora, or social media groups in your industry – these often reveal exactly how real users phrase their problems and product searches. For instance, on Reddit a user might ask, “Anyone have recommendations for a vitamin that boosts energy but doesn’t disrupt sleep?” (as noted in a recent marketing analysis of ChatGPT’s shopping feature) ( [11] ). Such phrasing is gold for your content strategy, because it tells you the precise language and criteria (energy boost, no sleep issues) that matter to potential customers. It’s also instructive to study how AI itself breaks down queries. Unlike Google’s traditional approach of matching keywords to pages, ChatGPT’s web-enabled search (ChatGPT with browsing/SearchGPT) and similar AI systems decompose a complex question into sub-queries and scour multiple sources before synthesizing an answer ( [12] ) ( [13] ). For example, if a user asks: “I need a durable, colorful iPad case that isn’t too bulky,” a Google search might return shopping ads and a list of links, whereas ChatGPT will internally search for “durable colorful iPad case,” “best iPad case not bulky,” etc., and pull data from forums, blogs, product pages, and expert reviews – then summarize everything into a single recommendation ( [14] ) ( [15] ). AI has the ability to use not just official product info, but also unstructured content like forum discussions and Q&As, because it “reads” entire pages and understands context, rather than simply indexing keywords. In fact, ChatGPT’s search has been observed to rely heavily on forum posts, Reddit threads, and long-form articles when answering certain queries, even more so than standard search engines ( [16] ) ( [17] ). This means user-generated content and the broader conversation about your brand online (reviews, discussions) can directly influence AI outputs. Crucially, generative AI often prefers answering questions rather than just listing websites. Data from Baidu (China’s largest search engine) illustrates this preference on an international scale. In 2024, Baidu reported that about 18% of all searches on its engine were being answered directly by its AI “Smart Answer” feature, which is akin to Google’s featured snippets or SGE ( [18] ). These AI answers were triggered most by question-based queries – in one analysis, nearly 69% of searches phrased as a question (e.g. starting with “what is…”, “how to…”) returned an AI-generated answer, whereas generic product-name searches triggered AI answers less than 2% of the time ( [19] ) ( [20] ). In other words, both Western and Eastern search platforms are converging on the idea that if the user asks a clear question, an AI-crafted answer should appear. Marketers therefore need to meet users in their question format. If your content doesn’t address the questions people are asking, it’s invisible to these answer engines. To optimize for the AI era, empathize with your audience’s questioning style. In workshops or brainstorming sessions, put yourself in the customer’s shoes and formulate the kinds of detailed questions they might ask at different stages of the buying journey: Early research: “What are the benefits of solar … Read more

The rise of generative AI in search means that off-page SEO – the signals and content about your brand beyond your own website – has never been more critical. In traditional SEO, tactics like link-building and PR established authority and boosted rankings. In the AI era, those same off-page efforts take on new dimensions. Large language models (LLMs) and AI-driven search tools draw on vast swaths of web content to formulate answers. They tend to favor information from well-known, authoritative sources, and they often repeat the narratives and opinions prevalent across the web. As a result, building your brand’s authority off-site doesn’t just help with Google rankings – it can directly influence whether and how your brand is mentioned in AI-generated answers. In this article, we explore how to bolster off-page signals and brand authority in ways that resonate with AI models and generative search experiences. From digital PR and community engagement to leveraging reviews and open data, we’ll examine strategies (with real examples from 2024–2025) to ensure your brand is both trusted by AI and prominent in AI-driven results. We’ll also discuss tools for monitoring your brand’s presence in ChatGPT, Google’s Search Generative Experience, and other AI platforms, so you can continually refine your off-page strategy. The future of search visibility will be about more than just blue links – it will be about being part of the conversations and content that intelligent systems use to answer user questions. Digital PR for Authority in AI Search Off-page SEO has long been about establishing authority : earning backlinks, media mentions, and references from credible third-party sites. In the era of AI search, this digital PR aspect is even more crucial. LLMs like ChatGPT or Google’s generative search AI don’t literally calculate PageRank, but they are heavily influenced by what content they ingest and deem trustworthy. Generally, LLMs favor content from authoritative domains – those that are widely cited, well-known, or associated with expertise ( [1] ). This means securing mentions in trusted publications, getting experts to cite or quote your work, and having a presence on high-authority sites can increase the likelihood that AI models regard your brand as worthy of inclusion in answers.  The “Mentions” Currency: Unlike Google’s ranking algorithm which is built on links, AI models prioritize mentions and context in their training data ( [2] ). SEO expert Rand Fishkin describes it succinctly: “The currency of Google search was links… The currency of large language models is mentions (specifically, words that appear frequently near other words) across the training data.” ( [2] ) In practice, if your brand or product is frequently mentioned alongside important keywords or topics (especially on respected sites), an LLM is more likely to recall or include it when generating an answer about that topic. For example, if many articles and lists about “top project management tools” mention a particular software brand, an AI answer to “What’s a good project management tool?” might very well include that brand by default, due to the patterns learned from those sources.  Earning Credible Mentions: The key, then, is to get your brand talked about in the right places. Traditional PR techniques – press releases, thought leadership articles, expert interviews – can lead to coverage in news sites, industry blogs, or research papers. These are precisely the kind of high-authority, factual sources that AI models are trained on. A mention or quote in a New York Times article, a citation in a university study, or inclusion in a “Top 10” list on a reputable blog not only boosts your human credibility but also means that when an AI combs through text to answer a question, your brand has a foothold in that knowledge base. For instance, when OpenAI’s GPT-4 was asked about the “best brands for small business marketing,” it produced a list of well-known software brands with citations from Wikipedia and a NerdWallet review article ( [3] ). The brands that appeared – Constant Contact, Mailchimp, HubSpot, ActiveCampaign, etc. – were all those with strong digital PR: they are frequently reviewed by third parties and discussed in authoritative contexts. In the ChatGPT-generated excerpt below, we can see that these brands are surfaced with sources like Wikipedia and NerdWallet highlighted:  ChatGPT’s answer (April 2025) to “What are the best brands for small business marketing?” cites a NerdWallet affiliate review and Wikipedia as sources. The response lists only well-known brands in the marketing software space, reflecting a bias toward companies with significant off-page presence and coverage. Smaller or less-cited brands are absent, underscoring how AI answers gravitate to what authoritative sources have discussed ( [3] ).  Inclusion in such lists doesn’t happen by accident – it’s often the result of successful outreach and PR. A tactical example: if you run a boutique CRM software company, you’d want to be mentioned in “Best CRM” roundups on sites like PC Magazine, TechRadar, or relevant industry blogs. Even if those mentions start as part of an earned media effort (e.g., pitching your product for review or contributing an article), the long-term benefit is that when an LLM later “reads” those articles during training or via real-time retrieval, it learns that your brand is associated with that category and carries credibility. As Rand Fishkin advises, to get your brand into AI answers, you might need to “make sure that our brand is mentioned [on] the places on the web” that discuss your topic, even if “that’s a PR process and a pitch process” – it is absolutely worthwhile ( [4] ) ( [5] ).  Authoritative Domains and LLM Trust: Search engines have long used domain authority or E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals as a proxy for credibility. LLMs don’t have an explicit E-E-A-T score, but they implicitly reflect these signals by relying on authoritative text. For example, if your company’s research report is cited by Wikipedia or appears on a .edu site, an AI model will likely treat the information as more reliable. Indeed, marketers have observed that ChatGPT’s newer browsing or integrated versions heavily cite Wikipedia for company or product information ( [3] ). Being on Wikipedia (with a well-sourced page) thus becomes an off-page priority – it’s a seal of notability that not only helps with Google’s knowledge panels but also virtually guarantees an LLM like GPT knows about your brand. In April 2025, one … Read more

As search evolves with generative AI, the technical foundations of SEO are more important than ever. A website’s behind-the-scenes structure, performance, and metadata now influence not only traditional search rankings but also how content is selected and presented by AI-powered engines. In this article, we explore how to optimize your site’s technical setup for the age of generative search. We’ll cover maintaining crawlability (including new AI-specific crawlers), using structured data and clean HTML to speak clearly to algorithms, ensuring fast and user-friendly page experiences, and employing new techniques to control if and how your content appears in AI-generated answers. The goal is to give AI and search engines every possible clue to understand and trust your site – while avoiding pitfalls that could hide your content or misrepresent it. Technical SEO for generative search is largely an extension of core SEO best practices, but with fresh nuances. Think of it as laying a solid, machine-friendly foundation beneath your high-quality content. If content is king, technical SEO is the architect that builds the castle – and now that castle must be welcoming to AI “visitors” as well as human ones. By the end of this article, you’ll have a clear action plan on how to tune your site’s technical elements (from robots.txt to HTML tags to page speed) so that both search engine crawlers and large language models can access, interpret, and feature your content accurately. Let’s dive in. Ensure Crawlability and Access The first step in technical SEO – whether for classic search or generative AI – is to ensure your site can actually be crawled and indexed. Crawlability means that automated bots (search engine spiders and now AI crawlers) can discover and fetch your content easily. If your content isn’t accessible, it won’t appear in search results or AI answers, no matter how great it is. Thus, maintaining clean, open access for reputable crawlers is critical. Open Your Site to Search and AI Crawlers Review your robots.txt file and other bot controls to make sure you’re not inadvertently blocking important crawlers. Traditional search engines like Google, Bing, and others should of course be allowed to crawl key pages (unless there’s a specific reason to block something). In the context of generative AI, new crawlers have emerged that website owners should consider. For example, OpenAI introduced GPTBot in 2023, which seeks permission to scrape web content for training models like ChatGPT ( [1] ). Google similarly announced a user agent called Google-Extended to let site owners opt out of content being used for Google’s AI (such as Bard or the Gemini model) ( [2] ). Importantly, blocking these AI-focused crawlers is a strategic choice. If you allow GPTBot and similar bots, your content may be included in the training data of future AI models, potentially giving your brand visibility in AI responses. If you disallow them, you’re signaling that your content shouldn’t be used for AI training – which might protect your content from misuse, but also means AI models may “know” less about your site. For instance, The New York Times and other major publishers updated their robots.txt to block GPTBot and Google’s AI crawler in late 2023 amid concerns about uncompensated use of content ( [3] ) ( [4] ). According to an analysis in September 2023, about 26% of the top 100 global websites had blocked GPTBot (up from only ~8% a month prior) as big brands reacted to AI scraping ( [5] ). This blocking trend peaked around mid-2024 when over one-third of sites were disallowing GPTBot, including the vast majority of prominent news outlets. However, by late 2024 the tide shifted – some media companies struck licensing deals with AI firms and the block rate dropped to roughly one-quarter of sites ( [6] ). In other words, many sites initially hit the brakes on AI crawling, but some have since opened back up as the ecosystem evolved. There’s no one-size-fits-all answer here. Online marketers must weigh the pros and cons for their specific situation. If your goal is maximum exposure and you’re comfortable with your content being used to train or inform AI, then keeping the welcome mat out for GPTBot, Google-Extended, and similar bots is wise. On the other hand, if your content is highly proprietary or you have monetization concerns, you might choose to restrict these bots until clearer compensation or control mechanisms are in place. Just keep in mind that opting out only affects future AI training – if an AI model has already ingested your content, blocking now won’t make it “unlearn” it ( [1] ). And not every AI provider announces their crawler or respects robots.txt; by blocking the ones that do (OpenAI, Google), you’re at least signaling your preference to the major players (and perhaps to any others who choose to honor these signals) ( [7] ) ( [8] ). From a practical standpoint, auditing your robots.txt is easy and important. This text file, located at your domain’s root (e.g. yourwebsite.com/robots.txt ), tells crawlers what they can and cannot access. To allow OpenAI’s GPTBot full access, you could add rules like this: plaintext # Allow GPTBot to crawl the entire site User-agent: GPTBot Allow: /  If instead you decide to block an AI crawler, you’d use Disallow . For example, to block GPTBot or Google-Extended (Google’s AI crawler) across your whole site, your robots.txt would include: plaintext User-agent: GPTBot Disallow: / User-agent: Google-Extended Disallow: /  The snippet above outright forbids those bots from crawling any page on your site ( [3] ) ( [4] ). Y ou can also be more granular – for instance, allow them on most of your site but disallow in specific sections (like /private/ or a members-only blog). Just list the appropriate path under a Disallow for that user agent.  Remember: robots rules are public (anyone can view them), and compliance is voluntary. OpenAI and Google have stated their bots will follow these directives, but other AI projects might not. Still, it’s currently the best tool site owners have to request not to be scraped. Aside from these new AI-specific entries, ensure you’re not unintentionally blocking major search engine bots (Googlebot, Bingbot, etc.) in your robots.txt . Generative AI features like Google’s SGE (Search Generative Experience) draw on pages indexed in Google’s search index ( [9] ). If Googlebot can’t crawl and index a page because of a Disallow or other barrier, that … Read more

In the era of Generative Engine Optimization (GEO), crafting content requires a strategic blend of traditional SEO best practices and new techniques aimed at AI-driven search experiences. As search evolves from keyword-based queries to AI-generated answers, content marketers must adapt how they create and structure information online. This article explores how to produce content that not only ranks in classic search engines but is also favored and directly utilized by Large Language Model (LLM) systems like ChatGPT, Google’s Gemini-powered search, Bing Chat, Perplexity, Anthropic Claude, Meta’s LLaMA, xAI’s Grok, and other emerging AI tools. We will examine why demonstrating E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and originality is paramount, what types of content AI can’t easily replicate, how to format content for AI excerpting, the benefits of a conversational tone and FAQ-style organization, and the importance of continual content updates to remain relevant in the AI age. Throughout, we’ll include real-world examples and up-to-date statistics, and we’ll highlight tools and tactics for optimizing content to be discovered—and even quoted—by generative AI systems. By the end of this post, online marketing professionals will have a clear action plan for creating AI-optimized content that drives visibility and traffic in the generative search era. E-E-A-T and Originality as Differentiators One of the most critical factors for content success in generative search is adhering to Google’s E-E-A-T guidelines: Experience, Expertise, Authoritativeness, and Trustworthiness. High-quality, expert content matters more than ever because AI models tend to favor and surface information from sources that demonstrate credibility and depth ( [1] ) ( [2] ). In Google’s experimental Search Generative Experience (SGE) and “AI overviews,” for example, users noticed that well-known websites and brands (those with strong authority signals) were appearing prominently in the AI-generated answers ( [1] ). This suggests that Google’s algorithms, when selecting content to include in a generative snippet, lean heavily on perceived authority and trust – essentially, E-E-A-T still matters in the AI search era ( [3] ).  Illustration of Google’s E-E-A-T framework – Experience, Expertise, Authoritativeness, and Trustworthiness – as four pillars of content quality. In the AI-driven search landscape, content that demonstrates these qualities is more likely to be seen as reliable and thus favored by search engines and AI assistants. Ensuring your content showcases firsthand experience, expert knowledge ( expertise ), recognized authority, and solid trustworthiness can significantly improve its chances of being referenced in generative AI results. ( [4] ) ( [2] )  Why E-E-A-T is crucial for GEO: Generative AI tools like ChatGPT and Google’s upcoming Gemini model are trained on vast swaths of internet text. When these models formulate answers, they probabilistically draw on patterns learned from their training data. They don’t truly know which sources are authoritative, but their training process means that information echoed by many reputable sources – or by sources with strong digital footprints – carries more weight. Google’s own systems explicitly aim to surface high-quality info from reliable sources and to downplay content that lacks authority or trustworthiness ( [5] ) ( [2] ). In practice, this means that content written by recognized experts, content published on sites with a reputation for subject authority, and content that provides trustworthy, accurate information is far more likely to be selected by AI summarizers. As one industry guide noted in early 2024, Google is expected to “continue emphasizing E-E-A-T in content discovery results, whether in AI or regular search,” with the credibility of the page, author, and website all factoring into what gets displayed ( [6] ). In short, demonstrating E-E-A-T in your content isn’t just about pleasing human readers – it directly influences whether an AI will treat your content as a trustworthy source worth including in an answer.  Originality and firsthand experience: A key aspect of E-E-A-T that **AI struggles to replicate is experience. Large language models can aggregate and rephrase common knowledge from the web, but they lack genuine firsthand experience or original insight. Content that showcases personal experience or unique expertise will stand out as something AI cannot simply generate on its own** ( [7] ). For example, a travel website that includes a blog post like “My 7-day trek through the Andes – lessons learned” (with vivid personal details, original photos, and first-person tips) is offering something qualitatively different from the generic travel summaries an AI might produce from Wikipedia and standard tourist info. That human touch – the experience – makes the content more trustworthy and valuable. In Google’s ranking guidelines, “experience” was added as a new facet to E-A-T in 2022, precisely to encourage content creators to share firsthand experiences when relevant (such as a product review written by someone who has actually used the product, or an analysis by a professional who has hands-on experience in the field). In the generative age, we can expect AI-driven search to similarly reward content that carries the stamp of personal experience and originality. Not only do human reviewers value this, but AI models trained on vast data can often detect when content is merely rehashing generic facts versus when it provides a novel perspective or real-life example. Indeed, marketers have found that AI-generated text often feels generic or “flat” without human insight – one marketing firm noted that “without a human perspective, AI-generated content can feel generic—or worse, misinformed”, underscoring that authentic human input is still critical ( [8] ).  Authoritativeness and branding: Another facet of E-E-A-T is Authority, which in practice can relate to your brand’s reputation or your authors’ credentials. In the context of GEO, authority might be signaled by factors like: Are other sites referencing your content? Do your pages have quality backlinks? Is your brand well-known in the industry? Are your experts cited elsewhere? All these contribute to whether an AI might “think” of or prefer your content when constructing an answer. For instance, SEO professionals observed in 2024 that “big name” websites had an edge in Google’s AI overviews – likely because Google’s system associated those domains with authoritative content ( [3] ). This doesn’t mean smaller sites can’t get included, but it means establishing topical authority is key. Writing thorough, well-researched content and getting recognized (via references, mentions, or shares) by others in your field will help build that authority over time. Even on AI platforms like ChatGPT, which don’t link out by default, the underlying model is more likely to produce information it saw on authoritative sites during training. Thus, being cited on high-authority platforms (news sites, respected blogs, scholarly articles, etc.) indirectly influences LLMs. For example, if your company … Read more

In this article, we explore the rise of new large language models (LLMs) beyond the early leaders like OpenAI’s GPT-4. We examine Anthropic’s Claude (known for its large context window and safety measures), Meta’s LLaMA (an open-source model family powering countless niche applications), and xAI’s Grok (Elon Musk’s social-media-trained chatbot with an irreverent style). We compare their key features – from context length and knowledge updates to integration channels – and discuss what these differences mean for Generative Engine Optimization (GEO). Finally, we consider how a multi-model ecosystem is emerging, where no single AI assistant dominates all queries, requiring marketers to optimize content for a variety of AI systems globally. Anthropic’s Claude: Long Memory and Safety-Focused Design Anthropic’s Claude is often mentioned in the same breath as GPT-4, positioned as a major competitor in advanced AI chatbots. Claude’s defining feature is its massive “memory” or context window , which allows it to read and retain extremely large amounts of text in one conversation. In mid-2023, Anthropic expanded Claude’s context window from an already-impressive 9,000 tokens to 100,000 tokens , roughly 75,000 words ( [1] ). This means Claude can ingest entire books or lengthy documents at once without losing track. For example, Anthropic demonstrated Claude reading The Great Gatsby (72K tokens) and correctly identifying a single modified line in just 22 seconds ( [2] ). Such capability far exceeds the context limits of most other models at the time, enabling deep analysis of long-form content in one go. In fact, newer Claude versions (Claude 2.1 and “Claude 4” in 2024–25) have pushed context limits even further – reportedly up to 200K tokens (around 150,000 words) in some variants ( [3] ) ( [4] ) – making Claude especially suited for tasks like reviewing lengthy reports, synthesizing information across multiple files, or digesting entire websites’ content in one answer. Claude is not just about length; it’s also designed with a strong emphasis on safety and transparency . Anthropic developed Claude using a technique called “Constitutional AI,” which means the model follows a set of guiding principles (a kind of internal AI constitution) to ensure it produces helpful and harmless responses ( [5] ). This approach makes Claude more cautious and polite in tone, avoiding toxic or disallowed content more rigorously. For enterprise users and marketers, this reliability is a selling point – Claude aims to minimize the risk of offensive or wildly incorrect outputs through these built-in safety rules. Anthropic often touts this as a differentiator, appealing to businesses that require an AI assistant aligned with ethical guidelines and brand safety. Another strength of Claude is how it’s being integrated into real-world productivity tools , enhancing its practical utility. For instance, Slack’s workplace messaging platform has incorporated Claude as an AI assistant for teams. In Slack, users can @mention Claude to summarize long chat threads, answer questions, or pull information from documents and websites shared in the channel ( [6] ). Because of Claude’s large memory, it can remember an entire Slack conversation history, meaning it can provide context-aware answers even in extended discussions. Notably, Claude can also fetch content from a URL you share (when explicitly asked), allowing it to include up-to-date information from the web in its responses ( [6] ) ( [7] ). However, Claude’s core knowledge (from its training data) has a cutoff of roughly 2021–2022, so by itself it “has not read recent content” and “does not know today’s date or current events” ( [7] ). The Slack integration mitigates this by letting Claude read provided links or user-supplied text, effectively performing on-demand retrieval. Anthropic assures Slack users that any data Claude sees in your workspace is kept private and not used to further train the model ( [8] ), addressing data security concerns for companies. This use case – AI assistance in corporate chat – plays to Claude’s strengths in summarization and following instructions across lots of text, like policy documents or project notes, within a controlled environment. Claude has also been made available through Quora’s AI chatbot hub called Poe . Poe is a platform that offers access to multiple AI models (GPT-4, GPT-3.5, Claude, etc.) in one app, allowing users to converse with each and even compare answers. Quora’s team, in partnership with Anthropic and Google Cloud, deployed Claude on Poe to great effect – millions of users’ questions are answered by Claude daily on that platform ( [9] ). According to Quora’s Product Lead for Poe, Claude delights users with its “intelligence, versatility, and human-like conversational abilities,” powering a broad range of queries from coding help to creative writing ( [9] ). Notably, Quora leverages Claude’s strength by using it for complex tasks like generating interactive app previews and even coding assistance within Poe ( [10] ). The fact that Quora felt the need for a multi-model approach in Poe (offering Claude alongside OpenAI and other models) underscores how Claude provides unique value – often excelling at detailed, structured answers and large-scale analysis – that complements other chatbots ( [11] ). From a GEO (Generative Engine Optimization) perspective, Claude’s rise means that content creators should be mindful that Claude can consume very large chunks of content in one go . If a user query on Slack or Poe triggers Claude to analyze “your entire report” or a 50-page whitepaper on your site, Claude can actually do it – and quickly. This raises interesting opportunities: well-structured, comprehensive content might be fully digested by Claude, potentially allowing more nuanced answers to incorporate your details. For example, a marketer could upload a lengthy product manual or a series of blog posts into a Claude-powered system and get a synthesized answer that weaves together points from all of them. If your content is rich and authoritative, Claude might present a thorough summary of it (though caution: it may not cite you unless the interface is designed for that). The flip side is that if a competitor has more concise, well-organized summaries , Claude might favor those summaries internally when formulating an answer because it can easily traverse hundreds of pages. Thus, ensuring that important information is not buried too deep in a bloated document can help – even though Claude can read it all, you want your key points to stand out clearly for any AI summarization. Claude’s safety-first orientation also implies that “black-hat” manipulative tactics are likely to backfire. Its Constitutional AI might refuse to output content that seems biased or … Read more

The search landscape is no longer dominated solely by Google. A new wave of AI-powered search engines and assistants has emerged, offering users conversational answers, summaries with citations, and innovative features that challenge the traditional “ten blue links” model. This article explores the key alternatives beyond Google – from Microsoft’s Bing Chat to independent platforms like Perplexity AI and others – and examines what they mean for marketers in the era of Generative Engine Optimization (GEO). We’ll look at how these AI-driven search tools work, real-world adoption trends in 2024–2025, and practical steps for optimizing content for them. While Google still commands the lion’s share of search traffic, early adopters of AI search are often tech-savvy and influential. Understanding these platforms now can give marketers an edge (and many tactics will carry over to Google’s own AI search features).  Bing Chat’s GPT-4 Integration and Influence Microsoft made headlines in early 2023 by integrating OpenAI’s GPT-4 into Bing and launching the new Bing Chat feature ( [1] ). This marked one of the first major attempts to embed generative AI directly into a mainstream search engine. Instead of the familiar page of ranked links, Bing’s AI mode delivers a conversational answer that synthesizes information from across the web and presents it in narrative form, with footnote citations linking to sources ( [2] ) ( [3] ). Users can type natural language questions and receive an AI-generated summary or explanation, often combining information from multiple webpages. Crucially, Bing’s implementation addresses a key concern with generative AI: each answer includes references to the websites used, usually denoted by numbered footnotes that users can click for verification ( [2] ). This not only lends transparency and credibility, but also creates a pathway for web traffic – if a user wants more detail or to verify the AI’s answer, they can click the citation and visit the source site.  How Bing Chat Works: On the backend, Bing Chat marries Microsoft’s search index (Bing’s web crawling and indexing capabilities) with the language understanding of GPT-4 ( [3] ). Essentially, when a user asks a question, Bing’s system retrieves relevant content from live web results, then the GPT-4 model “reads” those results and composes a coherent answer in real time, complete with supporting references. This is a form of Retrieval-Augmented Generation (RAG) in action. Because it uses current web content, Bing Chat can provide up-to-date information, overcoming the “knowledge cutoff” limitation of static trained models. Microsoft initially rolled out Bing Chat via a limited preview (waitlist) in February 2023 and later expanded access, including integration into the Edge browser’s sidebar and Bing’s mobile apps ( [4] ). By mid-2023, Bing Chat was accessible to all users of Microsoft Edge, effectively reaching millions by being baked into the default Windows web experience.  A New User Experience: The introduction of Bing’s chat mode fundamentally changes how some searches work. Users can now have a multi-turn conversation refining their query – asking follow-up questions or for clarifications – much like they would with a human expert. This conversational capability means search is becoming less of a one-and-done query and more of an interactive dialogue ( “from queries to conversations”). Bing’s AI remembers context within a session, so it can tailor answers based on previous questions, making the experience more intuitive and personalized ( [5] ). From the user’s perspective, Bing Chat offers several distinctive benefits:  Direct Answers, Fewer Clicks: Instead of scanning multiple sites, the user often gets the answer they need summarized in one go. Bing’s AI-generated response often fulfills the query without the user needing to click any result ( [6] ). For example, a query like “What are the health benefits of green tea?” might yield a concise paragraph citing a few authoritative health websites, rather than ten separate links to sift through. This improves convenience but also means reduced click-through to websites overall, as the AI has “pre-read” the content for the user.  Citation Footnotes and Transparency: Unlike a raw ChatGPT response, Bing’s answers clearly highlight their sources. Small superscript numbers link to references, and users can expand a references pane or hover to see the URLs. For instance, Bing might answer a question and include “ [1] [2] [3] ” footnotes – clicking those reveals the websites (like “MayoClinic.org” or “Healthline.com”) it pulled information from ( [2] ). This system was lauded for bringing some credibility and accountability to AI answers ( [7] ). It also provides an opportunity for content publishers: if your site is one of those cited, you gain visibility and potential traffic.  Dynamic Query Refinement: Users can ask follow-ups like “What about for weight loss specifically?” and Bing’s AI will remember you were asking about green tea and adapt the answer ( [8] ). This conversational refinement means long-tail questions that might not have a pre-written FAQ on your site could still be answered by the AI drawing from your content (if your content is comprehensive).  Integrated Visuals and Features: Microsoft has enhanced Bing Chat with multimedia and interactive elements. The AI can deliver charts, graphs, or images alongside text when relevant ( [9] ). It can also perform certain actions like generating product comparison tables or integrating with shopping data. This blurs the line between search, content, and commerce within the chat interface. For example, an AI answer about “best smartphones under $500” might show a comparison chart of models with specs, plus links to buy – all without leaving the search page.  Influence and Adoption: Bing’s move gave it a burst of attention. Within a month of launch, Microsoft announced Bing had reached 100 million daily active users, an all-time high, partly thanks to “the million+ new Bing preview users” trying the chat feature ( [1] ) ( [10] ). To put that in perspective, Google has over 1 billion daily users, but for Bing it was a significant milestone ( [11] ). Microsoft noted that about one-third of Bing Chat users were new to Bing, indicating the AI feature attracted people who normally defaulted to Google ( [12] ). On average, users were engaging in roughly 3 chats per session, with over 45 million chats conducted in the first month ( [12] ) – evidence that many found the conversational search useful enough to dig deeper. However, Bing’s overall search market share remains relatively small. By late 2023, estimates put Bing at around 3–4% of global search queries, barely up from pre-chat levels ( [13] ). For example, … Read more

In this post, we explore Google’s journey from traditional search engine optimization (SEO) toward generative search. We trace how early direct-answer features like featured snippets and voice responses (Answer Engine Optimization, or AEO) set the stage for today’s AI-driven Search Generative Experience (SGE). We also examine Google’s AI chatbot Bard and the upcoming Gemini model – key components in Google’s response to the large-language-model (LLM) revolution. Importantly, we’ll discuss how marketers can optimize for these AI-powered results using proven SEO best practices, and analyze the opportunities and threats generative search presents for various industries (including e-commerce, SaaS, and B2B). Featured Snippets and the Roots of AEO (Answer Engine Optimization) Featured snippets were Google’s first major step toward providing direct answers on the results page. Introduced around 2014, featured snippets are concise answer boxes that appear at the top of search results, pulling an excerpt from a relevant webpage. For example, a query like “What is a marketing funnel?” might show a boxed summary definition drawn from a marketing blog, above the usual list of blue-link results. Around the same time, Google’s Knowledge Graph and “People Also Ask” boxes also emerged, offering factual summaries and related Q&A pairs directly on the search page. These features signaled a shift: Google was no longer just a gateway to information, but increasingly an answer engine delivering solutions immediately within the search interface. This shift gave rise to the concept of Answer Engine Optimization (AEO). First coined by forward-looking SEOs in the mid-2010s, AEO refers to tailoring your content to be selected as a direct answer in search results (featured snippets, answer boxes, voice assistant replies, etc.). Unlike traditional SEO which prioritizes earning clicks to your site, AEO is about visibility even without clicks – making your content the authoritative answer that Google (or Siri, Alexa, etc.) provides. Key AEO tactics included: Structuring content around questions and answers: Content creators learned to incorporate common user questions as headings, immediately followed by succinct, factual answers. For instance, an FAQ page might ask “How does X product work?” with a 2-3 sentence answer directly below. This format increases the chance that Google will excerpt that answer in a snippet or voice response. Using lists, tables, and steps: If a query is looking for a list (e.g. “top 5 CRM software features”) or a how-to procedure, formatting the answer as a bullet list or step-by-step numbered list improves snippet eligibility. Early research showed that Google often pulled numbered lists for “How to…” queries and tables for data-driven queries. Schema markup and structured data: Webmasters began adding structured data (using Schema.org tags like FAQPage, HowTo, Recipe, etc.) to make the page’s Q&A content machine-readable. This helps search engines identify and trust the content format, increasing the likelihood of it being featured. For example, marking up an FAQ section with <FAQPage> schema can directly feed Google’s Q&A features. Voice search optimization: As voice assistants became popular, AEO overlapped with voice SEO. The answers needed to be conversational and concise because devices like Google Home would read them aloud. This reinforced writing in a natural, easy-to-understand tone that still contained the key answer in the first sentence or two. People-first, authoritative content: AEO still required quality. Google favored sources that demonstrated expertise and authority (what later would be codified as E-E-A-T: Experience, Expertise, Authoritativeness, Trustworthiness). So, while formatting was important, content creators also focused on accuracy, citing reputable facts, and providing genuinely helpful answers to earn Google’s confidence. Together, these practices formed the foundation of AEO. Marketers recognized that search was evolving “from a gateway to a destination” – often answering users’ needs without a click. For example, on mobile or voice, a user might get the full answer read out, never visiting the site. The upside was that if your content was the one featured, your brand gained high visibility and implied endorsement by Google. The downside was fewer clicks and less control over how your content was presented. By the late 2010s, the question for SEO professionals had expanded from “How do I rank #1?” to “How do I become the source that Google’s AI or answer box cites?”. In essence, AEO was an early playbook for what has now become Generative Engine Optimization (GEO) – optimizing content for AI-generated answers. It’s worth noting that many industries started leveraging AEO techniques. E-commerce sites began structuring product pages to answer common questions (return policies, materials, sizing) directly on-page, hoping to capture “People Also Ask” spots or voice answers about their products. B2B and SaaS companies invested in rich knowledge bases and blog content targeting long-tail questions (e.g. “How to improve team productivity in agile software development”) so that their expertise could appear in featured snippets. In doing so, they not only aimed to drive traffic but also to build brand authority by being the answer users hear or see. This was critical in fields where trust and thought leadership drive sales: if a prospective client keeps seeing a SaaS company’s name popping up in answer boxes about, say, data security best practices, it subtly positions that company as an authority before the user even visits their site. Crucially, AEO set the stage for today’s AI-driven search. It trained a generation of content creators to think in terms of answers, not just keywords. The lessons learned – about concise phrasing, structured information, and anticipating user questions – are directly applicable to optimizing for AI summaries and chatbots. As we’ll see, Google’s latest evolution, the Search Generative Experience (SGE), can be viewed as the next logical step in this “answer-first” evolution of search. SGE uses advanced AI to synthesize answers from across the web, but it still relies on well-structured, authoritative source content – exactly the kind of content that AEO practitioners excel at producing. Google’s Search Generative Experience (SGE) By late 2022 and early 2023, the search landscape was shaken by the rise of powerful LLM-based tools like OpenAI’s ChatGPT. Users flocked to these AI chatbots for quick answers and advice, raising an existential question for Google: Would … Read more

ChatGPT’s Introduction: The Breakthrough Chatbot in Mainstream AI Q&A In late 2022, OpenAI’s ChatGPT burst onto the scene as a conversational AI that could answer questions and perform tasks through a natural dialogue interface. Its impact was immediate and massive. Within just two months of launch, ChatGPT’s user base skyrocketed to an estimated 100 million monthly active users – making it the fastest-growing consumer application in history at that time ( [1] ). By early 2025, ChatGPT had become one of the top 10 most visited websites in the world, attracting roughly 4.8 billion visits per month ( [2] ). To put that in perspective, nearly half of consumers (48%) in one late-2024 survey reported using ChatGPT or a similar AI tool in the past week alone ( [2] ). Such rapid adoption indicates that ChatGPT introduced generative AI to mainstream audiences on an unprecedented scale.  ChatGPT as an “Answer Engine.” Unlike traditional search engines that provide a list of links, ChatGPT delivers a single, synthesized answer or solution in response to a natural-language prompt. Users have embraced this answer engine style for a staggering range of tasks. For example, software developers and students turn to ChatGPT for coding help or debugging advice instead of searching forums; in fact, the Q&A site Stack Overflow saw significant traffic declines (a 14% drop in one month) as coders opted to “just get an answer” from ChatGPT rather than browsing forum threads ( [3] ) ( [4] ). People also use ChatGPT for content creation (drafting emails, essays, marketing copy), for learning about complex topics through simple explanations, and even for personal tasks like brainstorming gift ideas or planning trips. A late-2024 retail survey found that 51% of shoppers had tried ChatGPT or similar generative AI tools in their daily lives (up from 29% a year prior) ( [5] ), using them for product research, personalized buying guides, and recipe discovery. In short, ChatGPT’s ability to engage in plain English (or any language) conversation and provide direct answers has made it a go-to digital assistant for millions of users.  From Novelty to Ubiquity. ChatGPT’s rise also benefited from massive public curiosity and media attention. It was often described as “AI that can answer anything”, drawing users who tested it on everything from trivial questions to professional work. By mid-2023, businesses began exploring ChatGPT’s potential, and by 2025 about 28% of U.S. workers reported using ChatGPT in their jobs (up from just 8% in early 2023) ( [6] ) ( [7] ). The user demographic skewed young and educated at first – a Pew Research survey in early 2025 found 58% of Americans under 30 had used ChatGPT (compared to 33% of those 50 and older) ( [8] ) ( [9] ) – but awareness and usage have grown across all groups. Notably, ChatGPT’s launch prompted a public discourse about AI’s capabilities and risks (e.g. schools worried about cheating, publishers about content scraping), yet this did little to dampen enthusiasm. OpenAI capitalized on the momentum by continually improving the model (releasing GPT-4 in 2023) and introducing new features (like image/voice input and third-party plugins, discussed below). Each update expanded what ChatGPT could do, further entrenching it as a versatile digital assistant. In summary, ChatGPT was the breakthrough that familiarized the broader public with AI-powered Q&A. It demonstrated, at scale, that many search or help tasks could be accomplished through a conversation with an AI instead of a traditional search query. This paradigm shift – from typing keywords into Google to asking a question to an AI – has profound implications. For users, it offers convenience and efficiency; for content creators and marketers, it foreshadows a new landscape (Generative Q&A) in which providing information or solutions without a click to one’s website becomes the norm. In the following sections, we delve into how ChatGPT’s usage differs from traditional search and what that means for those seeking to optimize content in this new era. ChatGPT vs. Traditional Search: A New User Behavior ChatGPT introduced a fundamentally different user experience compared to traditional search engines like Google. Instead of entering terse keywords and browsing a list of blue links, users pose complete questions or requests in natural language – for example, “How can I improve my website’s SEO?” – and receive a single, coherent answer composed by the AI. This difference has led to distinct user behaviors and expectations: Conversational Queries: Users can ask ChatGPT questions in a straightforward, conversational manner, including follow-up questions to probe deeper or clarify – much like having a dialogue with an expert. By contrast, traditional search often requires iterative keyword tweaking and filtering through multiple results to piece together an answer. With ChatGPT, the first result is often the final result, since the AI crafts a synthesized response drawing from its vast training knowledge. One Answer vs. Many Links: Perhaps the biggest shift is that ChatGPT usually provides one answer (occasionally with multiple suggestions or options within it), whereas Google provides dozens of hyperlinks for the user to choose from. For users, this one-stop answer can be appealing – it’s fast and requires minimal effort to get information. In a controlled comparative study, participants using ChatGPT completed information-finding tasks significantly faster than those using Google Search, spending minutes less on average per task ( [10] ). They also issued fewer or comparable follow-up queries, but in a more conversational style (longer, detailed questions rather than terse keywords) ( [11] ). Despite having fewer sources to cross-check, users in that study rated ChatGPT’s information quality higher and found the experience more useful and satisfying than a standard web search ( [12] ). This highlights how a well-written, consolidated answer can trump an array of search results in perceived value. Interactive Refinement: With search engines, refining a query means trying a new search or clicking on advanced filters. With ChatGPT, users can simply ask the AI to adjust the answer: “What if my budget is only $500?” or “Can you clarify that last point?” The AI remembers the context of the conversation and tailors its next response. This multi-turn interactivity makes information retrieval feel like a conversation, reducing the friction of starting new searches from scratch. It also enables users to explore … Read more

Large Language Models (LLMs) like OpenAI’s GPT-4, Google’s Gemini, Anthropic’s Claude, Meta’s Llama 2, and the new xAI Grok are transforming how information is found and delivered. Unlike traditional search engines that index and retrieve web pages, LLM-powered tools generate answers by predicting text based on patterns learned from vast training data. This fundamental difference has big implications for digital marketing and SEO strategy. In this chapter, we’ll explore how LLMs work – from their training and knowledge limitations to issues like hallucination, memory, and relevance – and what marketers need to know to optimize content in this new era of Generative Engine Optimization (GEO). Training Data and Language Prediction Modern LLMs are built on transformer neural network architectures and are trained on enormous datasets of text (web pages, books, articles, forums, and more). Models such as GPT-4, Google’s Gemini, and Meta’s Llama owe their capabilities to ingesting hundreds of billions of words from diverse sources ( [1] ). During training, the model learns to predict the next word in a sentence, over and over, across countless examples. In essence, an LLM develops a statistical understanding of language: it doesn’t search for answers in a database at query time, but rather synthesizes a likely answer word-by-word based on the patterns it absorbed during training. For marketers, this distinction is crucial. It means that unlike a search engine that might surface your exact webpage if it’s deemed relevant, an LLM might generate an answer using information from your content (perhaps paraphrased or summarized) without directly quoting or linking to it. The model focuses on producing a fluent, relevant response – not on attributing sources by default.  Training on vast text corpora – OpenAI’s GPT-4, for example, was pre-trained on “publicly available internet data” and additional licensed datasets, with content spanning everything from correct and incorrect solutions to math problems to a wide variety of ideologies and writing styles. This broad training helps the model answer questions on almost any topic. Google’s Gemini, similarly, has been described as a multimodal, highly general model drawing on extensive text (and even images and code) in its training. Meta’s Llama 2 was trained on data like Common Crawl (a massive open web archive), Wikipedia, and public domain books. In practical terms, these models have read a large portion of the internet. They don’t have a simple index of facts, but rather a complex probabilistic model of language. One implication is that exact keywords matter less in LLM responses than overall content quality and clarity. Since an LLM generates text by “predicting” a reasonable answer, it might not use the exact phrasing from any single source. This means your content could influence an AI-generated answer even if you’re not explicitly quoted, provided your material was part of the model’s training data or available to its retrieval mechanisms. It also means an LLM can produce answers that blend knowledge from multiple sources. For example, ChatGPT might answer a question about a product by combining a definition from one site, a user review from another, and its own phrasing – all without explicitly telling the user where each piece came from. As a marketer, you can’t assume that just because you rank #1 for a keyword, an LLM will present your content verbatim to users. Instead, the model might absorb your insights into a broader answer. This elevates the importance of writing content that is clear, factually correct, and semantically rich, because LLMs “care” about coherence and usefulness more than specific keyword frequency.  LLMs synthesize rather than retrieve. Traditional search engines retrieve exact documents and then rank them. LLMs like GPT-4 generate a new answer on the fly. They use their training to predict what a helpful answer sounds like. As an analogy, think of an LLM as a knowledgeable editor or author drafting a new article based on everything they’ve read, rather than a librarian handing you an existing book. This is why LLM answers can sometimes feel more direct or conversational – the model is essentially writing the answer for you. It’s also why errors (hallucinations) can creep in, which we’ll discuss later. From an SEO perspective, this generative approach means that high-quality, well-explained content stands a better chance of being reflected in AI-generated answers than thin content geared solely to rank on Google. The model might not use your exact words, but if your page provides a clear, authoritative explanation, the essence of that information may inform the AI’s response. Conversely, stuffing pages with repetitive keywords or SEO gimmicks is less effective, because the LLM isn’t indexing pages by keyword; it’s absorbing content for meaning and then later recalling the meaning more than the literal words. It’s worth noting that LLMs are extremely large – GPT-4 is estimated to have over 1.7 trillion parameters (the internal weights that store learned patterns). These parameters encode probabilities for word sequences. When an LLM answers a query, it starts with the user’s prompt, and then it internally predicts a sequence of words that statistically and contextually fit. The self-attention mechanisms in transformers allow the model to consider relationships between words and concepts even if they are far apart in the text. For example, if a user asks “How does a hybrid car work?”, the model doesn’t search for a specific document. Instead, it uses its trained neural connections (built from seeing millions of words about cars) to produce an explanation, perhaps describing the battery, electric motor, and gasoline engine in seamless prose. It might have “seen” text about hybrid cars during training, but it’s not copying one source – it’s generating new sentences that sound like what it learned. This ability to synthesize means content creators should focus on providing comprehensive coverage of topics in a way an AI can easily learn from. In other words, ensure your content teaches well – because the better the AI can learn your information, the more likely it is to use it when generating answers.  Why this matters for marketers:  In the old SEO paradigm, one might obsess over exact-match keywords or getting a featured snippet. In the new GEO paradigm (Generative Engine Optimization), the emphasis shifts to being part … Read more

From Queries to Conversations: The Shift in User Behavior Not long ago, most people approached search engines with terse keyword strings – often just a few words. Today, thanks to generative AI, users are increasingly posing full questions and engaging in dialogue. Queries have become much longer and more conversational, resembling natural language requests rather than staccato keywords. For example, instead of searching “best cafe Athens,” a user might ask “What are the best quiet cafes in Athens near the Acropolis where I can work for a few hours?” and then continue refining that query in follow-up questions. In fact, one analysis found that prompts submitted to ChatGPT (a large language model chatbot) average around 23 words, versus roughly 4 words for classic search engine queries ( [1] ) ( [2] ). This highlights a dramatic expansion in query length and complexity as users “talk” to AI engines in complete thoughts. Such conversational querying goes hand-in-hand with multi-turn dialogue. Instead of treating search as a one-and-done transaction, users now often engage in back-and-forth interactions lasting several turns. They might ask an initial broad question, receive a synthesized answer from the AI, and then ask follow-ups to clarify or drill deeper – effectively having a mini-conversation. These dialogues can span several minutes as the AI remembers context from previous questions. For instance, someone planning a trip could begin with “What are the top attractions in Paris?” and after an answer, follow up with “Which of those are good for kids?” or “What’s the best time of day to visit the Louvre?” – expecting the AI to understand the context from prior turns ( [3] ) ( [4] ). This is a fundamental shift: search is becoming less about one query at a time and more about an interactive consultation. This change in behavior is reflected in usage statistics. The average session on ChatGPT’s website lasts many minutes, significantly longer than a typical Google search session. By early 2025, an average ChatGPT user session was about 7–15 minutes long, compared to the quick hit-and-run searches of old ( [5] ) ( [6] ). Users are willing to spend more time conversing with an AI if it means getting direct, context-rich answers. In practical terms, people treat AI chatbots like advisors or assistants – asking for explanations, advice, or creative ideas in a conversational tone – whereas a traditional search engine would have forced them to parse results and click multiple links themselves. Crucially, users now expect direct answers to their specific questions, not just a list of links. Generative AI can synthesize information from myriad sources and present a concise answer or summary. This “answer engine” behavior means that users increasingly phrase queries as full questions (even adding “please” or context for personalization) because they anticipate an explanation or solution from the AI. The old habit of typing cryptic keyword combinations to appease an algorithm is giving way to natural questions as if the user were chatting with a knowledgeable person. In short, search has evolved “from queries to conversations” – a fundamental change in user behavior driven by the rise of large language models. These trends are quantifiable. A 2024 study of millions of searches confirmed that query length on Google has started inching up as well, with significantly more searches containing 7–8 words than before ChatGPT’s debut ( [7] ). While the majority of Google queries remain short (under 4 words) ( [8] ), the increase in longer, question-like searches indicates that consumers are becoming more conversational even on traditional engines. The introduction of AI chat in search results (discussed later in this post) likely encourages this by showing users that detailed queries can yield direct answers. In summary, generative AI has begun to re-train users to “just ask” – using natural language and multi-step dialogue – rather than formulating search queries in the terse, keyword-centric style of the SEO era. ChatGPT’s Mainstream Breakthrough The paradigm shift in search arguably began with the public release of ChatGPT in late 2022. On November 30, 2022, OpenAI launched ChatGPT to the public, and within just 5 days it reached 1 million users, an unprecedented adoption rate ( [9] ) ( [10] ). By January 2023, ChatGPT had an estimated 100 million monthly active users, making it the fastest-growing consumer application in history at that time ( [11] ). This sudden mainstream exposure to AI-driven Q&A was a tipping point in search habits. Millions of people experienced, for the first time, what it’s like to get human-like, conversational answers from a machine. Instead of scrolling through search results, users could ask a question and receive a coherent, often detailed response in plain English (or whichever language they chose). ChatGPT effectively introduced the masses to a new way of finding information – via dialogue with an AI – and the idea quickly took hold. The timing was critical. Prior to ChatGPT, AI chatbots were mostly a curiosity (think of Siri, Alexa, or simpler chatbots) and not seen as direct search tools. ChatGPT, built on the powerful GPT-3.5 and later GPT-4 model, was capable of far more nuanced answers than any virtual assistant before. Launched as a free web tool, it rapidly penetrated popular culture – from students asking it homework questions to professionals using it for research or coding help. By early 2023, ChatGPT was receiving over 1 billion visits per month, and that grew to 4.8 billion monthly by late 2024 ( [12] ) ( [13] ). Such massive usage indicates that a substantial share of internet users had incorporated ChatGPT into their information-seeking routines. In everyday life, people were using ChatGPT to get summaries of complex topics, recipe ideas, travel itineraries, coding advice, medical explanations – queries they might have otherwise typed into Google, but now found more convenient to ask in a conversational way. ChatGPT’s mainstream breakthrough also forced a re-examination of what people were searching for. Interestingly, studies showed that only about 30% of the prompts people entered into ChatGPT resembled traditional search queries with clear informational or navigational intent ( [14] ) ( [15] ). The other 70% were new types of requests that don’t often appear in Google’s database – things like creative brainstorming, writing assistance, coding problems, personal advice, or complex multi-part questions. This suggests ChatGPT unlocked a latent demand for asking questions that people might never pose to a search engine (either because they wouldn’t get … Read more