The AI Search Revolution: Key Takeaways from Thomas Smith on Generative Engine Optimization (GEO)

By Mackenzie Hamilton

November 13, 2025

The world of digital visibility is undergoing a seismic shift. No longer is the coveted “ten blue links” at the top of a traditional Google search the only prize. Consumers are increasingly turning to AI tools like ChatGPT, Claude, and Gemini for direct, synthesized answers. The race to be cited by these “generative engines” has given rise to a new optimization discipline: Generative Engine Optimization (GEO).

In a recent live taping of the “Who Moved My Marketing?” podcast, AI and marketing expert Thomas Smith broke down this transition and unveiled his essential framework for adapting to the LLM search space.

Understanding the New Search Landscape: GEO vs. The Alphabet Soup

The core difference between traditional Search Engine Optimization (SEO) and the new world is the output. SEO focused on getting your link to rank high in a list. GEO focuses on getting your brand cited and recommended within a single, narrative-style AI-generated answer.

Smith clarified the emerging jargon:

GEO (Generative Engine Optimization): The practice of optimizing content to be visible, cited, and recommended by generative AI platforms like ChatGPT and Claude.
AEO (Answer Engine Optimization): A similar term, often used interchangeably with GEO, but sometimes referring more broadly to earning direct answers on any surface, including Google’s featured snippets.
AIO (AI Optimization/AI Overviews): A strategy to ensure your content is used to construct Google’s AI Overviews—the AI-generated summary that appears at the top of many Google search results.

This transition isn’t just theoretical; it’s a fundamental change in consumer behavior. With referral traffic from chatbots up 357% year-over-year, consumers are increasingly turning to ChatGPT, Claude, and Gemini for product research and recommendations. This is already leading to an “AI Dark Funnel” where brands lose visibility into the early research phases of the customer journey.

The Three Pillars of Generative Engine Optimization

To win in the GEO landscape, Smith outlined three non-negotiable pillars:

Pillar 1: Technical Clarity

Generative engines crawl the web. If your technical foundation is flawed, they may ignore you or, worse, misrepresent your brand.

Avoid Self-Sabotage: Early on, some companies blocked AI crawlers via their robots.txt file, fearing content theft. Today, this is a major barrier, as it blocks the very systems you need to cite you.
Prioritize Structured Data (Schema): Large Language Models (LLMs) rely heavily on Schema—machine-readable data that describes your business, products, social profiles, and company history. Unlike traditional SEO, where Schema was helpful but not essential, in GEO, it is critical for providing clear, undeniable context about your brand.

Pillar 2: Consistent and Rich Content

LLMs are “pattern recognizers trained on human language,” meaning they are story-driven and surprisingly “gullible” to the narratives they read.

Embrace the Narrative: Traditional SEO was often “writing for the algorithm” with keyword stuffing. GEO demands genuine, compelling storytelling. The story you tell about your brand, your products, and your people is what the LLM will repeat to its users.
Focus on Consistency: LLMs struggle to link information if it uses varying terminology. Use consistent language for your brand name, product names, and key personnel across all your controlled platforms (website, social profiles, product pages).
Go Long-Tail with Answers: Answer every possible question about your product and service on your own pages. Don’t be shy with comprehensive FAQ sections; this specific, detailed content allows the LLM to pull accurate, granular answers.

Pillar 3: Brand Authority and World Knowledge

AI models form an “opinion” of your brand based on what they read across the web, which they then integrate into their world knowledge.

Build Reputation: The brand pillar is about establishing credibility. This is achieved through legitimate PR, mentions in industry directories, and consistent visibility across high-authority platforms.
The World Knowledge Lag: While technical fixes can be seen quickly in live data, changing a model’s world knowledge is slow. Since training LLMs is expensive and only happens every few years, if an LLM forms a negative or non-relevant view of your brand early on, it could take years to correct. This is the argument for prioritizing GEO now, while the models are still in their infancy.

If an LLM forms a negative or non-relevant view of your brand early on, it could take years to correct. This is the argument for prioritizing GEO now, while the models are still in their infancy.

The Future: Paid GEO and Agentic AI

Smith highlighted two major coming changes:

The Rise of Paid GEO: Google is already experimenting with paid additions to its AI Overviews. For example, a search for “how to sear a steak” could include a sponsored recommendation for a cast iron skillet. The key insight here is that the AI chooses the ad based on informational content on the product’s page (e.g., the skillet company wrote an article about searing steak), reinforcing the need for every product page to include a story, not just specs.
Agentic AI: In the near future, AI systems will become “agents” that perform actions, like booking a flight or purchasing a product. In this scenario, the AI agent, not the human, becomes the customer, making it even more vital for brands to ensure their information is clear, consistent, and convincing to the machine.

The shift from the “ten blue links” to direct AI answers is an existential challenge for marketers. GEO is not just a new acronym. It’s a new philosophy centered on clarity, consistency, and a focus on human-quality storytelling that the new algorithms can genuinely understand. By addressing the Technical, Content, and Brand pillars now, marketers can secure their visibility in an AI-powered future.

Watch the full session:

About Mackenzie Hamilton

Mackenzie is a Marketing Specialist at EM Marketing, where she supports cross-functional teams with content creation, social media, email marketing, and campaign strategy. A UC Santa Barbara graduate, she brings a creative edge shaped by her background in Cultural Anthropology and Art History.

View all posts by Mackenzie Hamilton