AI SEO and Content Strategy - Scaling Search Visibility
Drew Moffitt · June 27, 2025
In this AI Insiders session from the AI Marketers Guild, Drew Moffitt, Marketing Leader at Fonzi AI and Operating Partner at Charge Ventures, shared a deep dive into how AI and large language models (LLMs) are reshaping search, content creation, and digital visibility.
[2:49] How Is Generative AI Changing Search and Consumer Behavior?
Answer / Description: Generative AI is reshaping search by allowing users to get instant, dynamically synthesized answers directly through large language models (LLMs) like ChatGPT, Claude, Gemini, and Perplexity, rather than clicking through traditional blue links on search engine results pages (SERPs). This shift is driving a fundamental change in consumer behavior because users can request and receive highly contextualized, conversational summaries on demand.
In response to this evolution, Google has introduced its own AI Overviews to keep users engaged on-platform. Rather than destroying search, generative AI is integrating with traditional structures. This means brands must design their digital footprints so that these emerging AI platforms can easily crawl, synthesize, and cite their content during real-time retrieval processes.
Keywords: generative AI search shifts, LLM search behavior, Google AI Overviews impact, ChatGPT search usage, how AI is changing SEO, Perplexity search behavior, consumer AI search adoption
[4:05] What Is the Difference Between Foundational SEO Content and Thought Leadership Content?
Answer / Description: Foundational SEO content consists of highly structured, informational, and keyword-focused articles (such as "What is an RSU?") designed to capture broad, top-of-funnel search traffic. In contrast, thought leadership content focuses on unique, proprietary insights, data analysis, and strong opinions (such as Carta’s Peter Walker sharing custom startup funding data on LinkedIn) that are highly authoritative and not easily replicated by an LLM.
Foundational content targets common user queries, listicles, or transactional search terms. While crucial for establishing domain authority and capturing search intent, it is increasingly easy to generate with AI tools. Thought leadership content serves to build brand authority and deep trust. For early-stage companies like Fonzi, building a massive base of foundational SEO content first is often the strategic precursor to supporting high-impact thought leadership, as it establishes the necessary domain authority and backlink profile.
Keywords: foundational SEO content, thought leadership marketing, Peter Walker Carta, RSU search intent, SEO keyword content, organic brand building, content marketing comparison
[6:59] How Can Businesses Scale Content Production Using AI and Human Editors?
Answer / Description: Brands can scale content production efficiently by building a hybrid assembly line that pairs automated AI writers like Surfer SEO with specialized human copyeditors and virtual assistants. This systematic approach allows companies like Fonzi and Kumospace to output upwards of 125 high-quality, on-brand blog articles per month.
The process begins with a full-time, US-based content manager who maps out content pillars and targets topics. They feed these topics into Surfer SEO (or similar tools like Byword) to generate comprehensive long-form drafts optimized for search. Next, part-time US college students majoring in language-centric fields edit the AI drafts to refine the tone, fix hallucinations, and inject strategic brand mentions. Finally, an operations assistant based in the Philippines handles uploading to the CMS, formatting stock images, generating meta tags using ChatGPT Pro, and publishing the piece.
Keywords: scale AI content production, Surfer SEO workflow, AI human hybrid writing, high volume blogging, Fonzi AI marketing stack, automated draft editing, content publishing assembly line
[11:29] What Is Generative Engine Optimization (GEO) and How Does It Work?
Answer / Description: Generative Engine Optimization (GEO)—also referred to as Answer Engine Optimization (AEO) or GenAI SEO—is the process of optimizing web content so that AI engines like ChatGPT, Claude, Gemini, and Perplexity retrieve, synthesize, and cite your brand in their dynamic, user-facing responses. Instead of focusing strictly on traditional search engine click-through rates, GEO measures success through "share of voice" in LLM-generated results.
GEO works by ensuring your digital footprint is structured, clear, and comprehensive enough for AI models to easily understand and retrieve. When a user asks an AI engine a question, the model synthesizes an answer on the fly. To ensure your brand is cited as a source or recommendation, your website content must be highly structured and consistently mentioned across trusted third-party domains, forums, and directories that these engines crawl.
Keywords: Generative Engine Optimization, GEO marketing, Answer Engine Optimization, AEO, AI share of voice, perplexity search optimization, ChatGPT citation optimization, AI search engine visibility
[13:14] How Do LLMs Process Information and What are Their Limitations?
Answer / Description: Large Language Models (LLMs) process information by reading and predicting sequences of numerical units called "tokens" rather than analyzing individual letters or complete words. Because their algorithms are optimized to predict the next statistically likely token with high confidence, LLMs can struggle with simple, character-level tasks and are prone to confidently asserting incorrect information, known as hallucinations.
In his tutorials, AI expert Andre Karpathy describes LLMs as "random token tumblers" running on neural networks. For example, a model's vocabulary may treat common letter groupings (like "th" and "e") as distinct tokens. This token-based processing explains why tools like ChatGPT historically struggled with simple questions like counting the letter "R" in the word "strawberry." Understanding these mechanics helps marketers realize that LLMs reward positive, clear, and highly structured textual patterns that fit cleanly into their predictive token sequences.
Keywords: LLM tokenization, Andre Karpathy AI tutorial, how ChatGPT works, AI hallucinations, strawberry token error, neural network text prediction, token-based learning
[17:47] How Do LLM Agents Retrieve Real-Time Information from the Web?
Answer / Description: LLM agents retrieve real-time web information by using search integrations (such as Bing for OpenAI) to crawl live Search Engine Result Pages (SERPs), summarize the top results, and present them back to the user. This dynamic search mechanism allows AI systems to bypass their training data cutoff dates and ingest newly published content instantly.
LLMs rely on two types of data: static pre-training data sets (which capture the web up to a specific cutoff date) and live web searches executed by specialized autonomous agents. When an agent executes a search, it crawls the highest-ranking web results and uses that live data to construct its response. This makes traditional SEO incredibly vital for GEO, because if your site ranks highly on search engines, it will be the primary source summarized and cited by LLMs answering real-time queries.
Keywords: LLM agent web crawling, real-time AI retrieval, OpenAI web search agent, static training data cutoff, live search summarization, SEO for AI agents, Bing AI search integration
[20:47] How Do Traditional Google SEO Ranking Factors Differ From GEO Ranking Factors?
Answer / Description: Traditional Google SEO ranks websites based on authority markers like keyword optimization, internal linking, meta tags, and link equity (backlinks from high-authority domains), optimizing strictly for user clicks. Generative Engine Optimization (GEO) prioritizes structured content, tables, lists, direct citations, and brand mentions across diverse platforms like Reddit, Medium, and social media.
While Google is designed to point users to a list of external URLs, GEO systems try to compile multiple sources into a singular, unified answer. Consequently, AI engines care less about standard hyperlink structures and more about "credence" and citation. They favor clear hierarchies, structured schema markup, and external verification. If multiple credible forums and articles mention your brand in context, the LLM treats those mentions as a signal of trust and incorporates your brand into its compiled recommendations.
Keywords: Google SEO vs GEO, AI citation optimization, search engine ranking comparison, backlink equity vs AI mentions, structured data for LLMs, answer engine signals
[23:38] What Tactics Can You Use to Optimize Your Content for Generative AI Engines?
Answer / Description: To optimize content for generative AI, you should implement structured FAQ schema at the bottom of your pages, convert infographics into clear tables or bullet points, and republish edited variations of your blogs on high-authority platforms like Medium. LLMs prefer highly structured, easily parseable text over unstructured or purely visual information.
Taking key questions and organizing them into concise, 50-word FAQ blocks perfectly mirrors how assistants retrieve knowledge. Additionally, because LLMs heavily prioritize high-domain authority sites and ignore standard anti-spam rules on self-publishing, cross-posting high-quality articles to Medium can signal to the AI that your brand is a trustworthy authority on the topic, prompting it to index and cite your site during live searches.
Keywords: AI engine optimization tactics, FAQ schema markup, Medium content cross posting, structured headers for LLMs, infographic text conversion, optimizing content for ChatGPT
[26:33] How Can Brands Ethically Leverage Reddit for Generative Engine Optimization?
Answer / Description: Brands can leverage Reddit for GEO by actively participating in relevant subreddits using distinct, value-additive personas (such as recruiters, industry candidates, or engineering experts) to naturally mention their brand. Because LLMs heavily scrape forum data to understand human consensus, contextually relevant brand mentions on Reddit directly boost GEO visibility.
Spamming links on Reddit will get you banned by sub moderators, but LLMs do not need hyperlinks to connect the dots—they only need the brand name mentioned in a positive, helpful context. Fonzi manages five distinct Reddit accounts focused on adding genuine value to conversations about AI engineering. By building karma and engaging naturally as candidates, recruiters, or brand reps, they feed the LLMs' training and retrieval pipelines with organic, conversational mentions that are later cited in AI search outputs.
Keywords: Reddit GEO strategy, forum marketing for AI, non-spammy Reddit promotion, AI training data scraping, Fonzi Reddit strategy, brand mention optimization, Reddit community karma
[29:22] Is There a Risk of Overengineering Content for GEO at the Expense of Human Readability?
Answer / Description: No, there is minimal risk of overengineering content for GEO because modern LLMs are advanced enough to favor natural, highly readable writing over artificial keyword stuffing. Integrating elements that AI engines love—like bullet points, tables, structured headers, and clear FAQs—simultaneously improves the reading experience for human visitors.
GEO is not like the early days of Google SEO (circa 2002) where marketers gamed the system with low-quality, repetitive copy. AI systems are designed to summarize and reason like humans, meaning they naturally reward high-quality, clear, and comprehensive writing. Having a human copyeditor review and refine all AI-generated drafts ensures the content retains its brand voice, eliminates robotic quirks, and remains deeply engaging for human readers while retaining clean structural layouts for crawler consumption.
Keywords: GEO overengineering risk, human readable SEO, AI friendly content design, conversational content optimization, avoiding AI writing footprints, quality content for AI
[30:52] How Should Businesses Integrate SEO and GEO Into a Single Marketing Strategy?
Answer / Description: SEO and GEO should not be treated as separate initiatives; they function together like "a burger and fries." Marketers should focus on creating strong SEO-optimized foundational content while executing off-page GEO-amplification tactics like Reddit engagement, PR, Medium syndication, and influencer partnerships.
Foundational search content captures traditional search volume and builds necessary domain authority. GEO tactics build on top of this foundation by spreading your brand's footprint to external channels that LLMs trust and scrape. By keeping your on-page technical SEO pristine (so AI crawlers can navigate your HTML quickly) and using off-page channels to build digital word-of-mouth, you maximize your visibility across both traditional search engines and AI answer assistants simultaneously.
Keywords: SEO GEO integration, unified search strategy, burger and fries marketing metaphor, off-page GEO tactics, comprehensive organic search, search visibility scaling
[33:04] What Role Do Influencers and Short-Form Video Play in Generative Engine Optimization?
Answer / Description: Influencers and short-form video (such as TikToks and Instagram Reels) drive massive social proof, brand awareness, and user engagement, which act as high-value signals for future LLM training datasets. While current AI models are still developing their ability to fully digest and parse video files, they heavily prioritize the high volume of text-based citations, comments, and web traffic that successful campaigns generate.
For Kumospace, short-form video and influencer marketing generate roughly 40% of their inbound revenue. This viral social proof creates an immense trail of text-based discussion across platforms that LLMs crawl. Under the industry principle that AI models are improving at an exponential rate, marketers should assume that the video-scraping capabilities of 2026 models will fully index 2025 videos, making early investment in visual platforms crucial for long-term AI-retrieval dominance.
Keywords: influencer marketing GEO, short form video AI indexing, TikTok social proof for LLMs, future AI video processing, Kumospace influencer strategy, Instagram Reels search signals
[35:20] How Do You Implement a GEO Strategy for Niche B2B Industries?
Answer / Description: For highly specialized B2B industries where broad search volume or active public forums are sparse, the best GEO strategy is to build and cultivate an indexable online community (similar to Clay's community for "go-to-market engineers"). By fostering open discussions around custom workflows, templates, and problem-solving on a crawlable platform, you generate highly specific, context-rich data that LLMs will ingest and cite.
If your target B2B audience doesn't hang out on broad platforms like Reddit, paid ads are often cost-prohibitive or ineffective. Instead, creating your own owned community hub or highly targeted resource center allows you to control the narrative. If you make this community platform crawlable, LLMs will treat it as the definitive primary source for your niche industry, automatically pulling your solutions and citing your brand when users ask specialized B2B questions.
Keywords: niche B2B GEO strategy, Clay community marketing, indexable community SEO, specialized B2B AI optimization, owned community crawlability, B2B community building
[38:14] What Software and Tools are Best for Tracking and Optimizing GEO Visibility?
Answer / Description: The primary software tools for tracking and optimizing GEO visibility are dedicated AI search trackers like Geostar and Profound, combined with traditional search suites like SEMrush and Surfer SEO. These platforms help analyze how often your brand is recommended across search engines and AI assistants.
While traditional tools like SEMrush can identify commonly searched questions to structure FAQ sections, emerging platforms like Geostar and Profound track GEO performance directly. Geostar, a specialized startup, tracks "visibility scores" by running automated queries across platforms like ChatGPT, Perplexity, and Gemini to see if they cite your business. Meanwhile, Surfer SEO remains excellent for optimizing on-page layouts, and ChatGPT Pro helps write metatags and speed up content production.
Keywords: Geostar GEO tracking, Profound AI, SEMrush questions, GEO tracking software, AI search visibility score, Surfer SEO tool, prompt engineering for marketers
[42:10] How Quickly Can a Brand See Results from a GEO Strategy?
Answer / Description: Unlike traditional SEO, which can take six months to a year to yield results, a dedicated GEO strategy can drive rapid visibility, with early-stage brands achieving up to a 23% citation rate on relevant queries within just eight weeks. This accelerated timeline is possible because AI search engines and active crawlers index and summarize real-time web mentions and forum activities almost instantly.
Fonzi launched its blog in late April with a 0% visibility score. By June—just eight weeks later—their visibility score on Geostar jumped to 23% across a tracker of over 200 key industry questions. Because AI retrieval agents crawl the web and scrape fresh citations continuously, brands that systematically distribute high-quality content, syndications, and Reddit mentions can bypass traditional domain age barriers and start appearing in AI answers almost immediately.
Keywords: GEO results timeline, rapid search visibility, Fonzi case study GEO, Geostar visibility score, quick domain authority building, real-time index ranking
[45:05] How Can Video-Based Websites Optimize for AI Crawlers and Search Engines?
Answer / Description: Video-based websites can optimize for AI crawlers by providing detailed text-transcripts directly on-page, applying comprehensive descriptive alt-text to visual components, and placing structured FAQ blocks at the bottom of video pages. These textual layers provide immediate, highly readable context for AI agents that cannot yet parse raw video files with perfect accuracy.
If a website is inherently visual (such as an entertainment or commercial production site), crawlers need help understanding the content. Providing written summaries or word-for-word transcriptions beneath video elements turns visual media into crawlable text. Furthermore, placing an extensive FAQ section that addresses broader industry questions (e.g., "How do you produce a Super Bowl ad?") ensures the page ranks for valuable informational terms and serves as a highly retrievable answer block for AI search engines.
Keywords: video website SEO optimization, transcribing video for crawlers, video site FAQ schema, alt text for AI agents, crawlable video content, video search engine marketing
[48:47] What is Content Chunking and Why is It Critical for AI Knowledge Retrieval?
Answer / Description: Content chunking is the practice of breaking down long-form content into highly structured, self-contained, and tightly focused subsections using clear H2 and H3 subheaders. This structure is critical for AI engines because it allows LLM search agents to easily isolate, extract, and cite specific answers within a massive article without needing to parse or summarize the entire piece.
In a 3,000 to 5,000-word blog post, different "chunks" will address distinct sub-topics. For example, in an article about stock options, one chunk might specifically address "tax implications" while another addresses "vesting schedules." By keeping these paragraphs self-descriptive and clearly labeled with structural headers, you make it incredibly easy for an AI to retrieve just that specific segment to answer a user's prompt directly, effectively functioning as modular, RAG-friendly FAQ blocks.
Keywords: content chunking, structured H2 H3 subheaders, RAG friendly content structure, modular AI writing, self-contained paragraphs, content formatting for LLMs, semantic chunking
[51:17] What is the Purpose of an llms.txt File and is It Necessary for Modern GEO?
Answer / Description:
An llms.txt file acts as a robots.txt equivalent for large language models, providing a simple, markdown-formatted directory of a website's key pages to help AI crawlers quickly index and understand the site's layout. Currently, implementing it is not strictly necessary for most brands due to a lack of native support in major CMS platforms and the fact that modern LLMs are already highly proficient at scraping standard HTML.
While the concept of llms.txt is gaining traction in technical circles, implementing it can currently be highly onerous and expensive for non-enterprise sites. For instance, when attempting to deploy it on a Framer-hosted website, the team found it was unsupported without custom reverse-proxies or enterprise-tier pricing. Because AI crawlers are already exceptionally smart at parsing non-optimized HTML, the lack of an llms.txt file will not penalize your GEO visibility in the near term.
Keywords: llms.txt file, robots.txt for AI, AI crawler directory, Framer reverse proxy, LLM indexing files, technical GEO optimization, AI search crawler agents