AI Founder: Focus on Human Behavior, Not Just AI Models
▼ Summary
– Google has integrated AI into its search algorithms for over a decade, making all search effectively AI search now.
– Grounding data is more critical than the LLM model itself, as indexed data provides up-to-date and accurate information for AI responses.
– Google maintains a massive data and index advantage, with ChatGPT’s results overlapping Google search about 50% of the time.
– Schema markup indirectly influences LLMs through retrieval layers and helps Google build a richer semantic understanding of the web.
– SEO success depends on understanding and attracting human search behavior, not debating terminology or chasing new tools.
Google continues its relentless pursuit of product excellence through constant iteration, adapting its interface to bridge the gap between traditional search and generative AI. Large language models and AI chatbots have become integral to SEO, yet many overlook that Google has woven artificial intelligence into its algorithms for years. The evolution isn’t about replacing search, it’s about enhancing how humans connect with information.
Ray Grieselhuber, CEO of Demand Sphere, emphasizes that grounding data holds far greater significance than the model itself. While AI models are trained for specific outcomes, the real competitive edge lies in the quality and breadth of indexed datasets. His research reveals that ChatGPT’s outputs align with Google’s results roughly half the time, compared to a much smaller overlap with Bing. This isn’t accidental, Google’s massive data repository remains an unmatched asset in the AI landscape.
The notion of “traditional search versus AI search” is outdated. As Grieselhuber notes, Google has been refining AI-driven search for over a decade. What truly matters is human behavior, the innate desire to seek, find, and understand. Marketers must focus on capturing attention through search behavior rather than debating terminology or chasing algorithmic shifts.
When it comes to schema markup, its role in LLM visibility is indirect but meaningful. While AI models don’t parse schema during training, structured data influences retrieval layers when these models pull from search results. Google’s push for schema adoption wasn’t solely for user benefit, it also streamlined how the company crawls and interprets the web, enriching its semantic understanding.
Looking forward, Grieselhuber predicts that SEO professionals will need to embrace a more user-centric, marketing-focused approach. The binary thinking that pits SEO against AI misses the point: people will always search, and their core behaviors won’t change. What evolves are the tools and experiences mediating that behavior.
Success in this new era depends on understanding user intent, experimenting with data, and delivering genuine value. Some users may eventually experience AI fatigue and return to familiar search interfaces, but most will fluidly move between generative AI and conventional search. Wherever they are, marketers must be ready to engage.
The industry is built for adaptation. SEOs are natural researchers and builders, equipped to navigate change faster than most. By focusing on human needs rather than technological labels, businesses can thrive amid ongoing transformation.
(Source: Search Engine Journal)
