Why Human Behavior Matters More Than LLMs, According to an AI Platform Founder

The Binary Thinking Trap That’s Killing Your SEO Strategy

Most discussions around AI and search fall into what Grieselhuber calls “binary thinking.” Either SEO is completely dead, or LLMs are the magical solution to all our ranking problems. This AI platform founder suggests both perspectives miss the mark entirely.

“Binary thinking about SEO being dead or LLMs being the new SEO misses that search behavior remains fundamentally unchanged. Humans are searchers who want to find information efficiently,” Grieselhuber explains in his analysis of current AI-driven SEO strategies.

The reality? According to data from BrightEdge, 68% of online experiences still begin with a search engine, and human psychology behind those searches hasn’t changed despite technological advances. People still want answers, solutions, and information: they’re just using different tools to find them.

A Reddit user in r/SEO captured this perfectly: “Everyone’s panicking about ChatGPT killing Google, but my customers still Google ‘best pizza near me’ the same way they did five years ago. The intent hasn’t changed, just the interface sometimes.”

Why Grounding Data Beats Model Sophistication Every Time

Here’s where most SEO for AI tools strategies go wrong: they focus on the flashy LLM capabilities rather than the foundational data these systems actually rely on. Grieselhuber’s research reveals a surprising finding: there’s significant overlap between ChatGPT and Google search results, suggesting both systems ultimately serve similar human information-seeking behaviors.

Grounding data matters more than model sophistication because:

• It reflects actual human search patterns and preferences
• It determines the quality and relevance of AI-generated responses
• It creates consistency across different AI platforms and traditional search engines

Research from Semrush shows that 76% of ChatGPT’s top results for commercial queries also appear in Google’s top 10, indicating that generative engine optimization and traditional SEO aren’t as different as many assume.

“The emphasis on grounding data reveals a crucial insight: even the most advanced AI systems are only as good as their understanding of real human patterns and preferences,” notes a recent study on LLM performance in search applications.

The Human Behavior Constants That Never Change

While technology evolves rapidly, certain aspects of human search behavior remain remarkably stable. This AI platform founder’s perspective challenges us to focus on these constants rather than chasing every new algorithm update.

Intent-Driven Search Patterns

According to Google’s own research, search intent falls into four primary categories that have remained consistent since 2003:

• Informational (70% of searches)
• Navigational (15% of searches)
• Commercial (10% of searches)
• Transactional (5% of searches)

These percentages have barely shifted despite the introduction of voice search, mobile-first indexing, and now AI-powered search tools.

Cognitive Load Preferences

Humans consistently prefer information that’s easy to process and understand. Whether someone’s using Google, ChatGPT, or asking Alexa, they want clear, concise answers that match their current context and expertise level. This principle drives successful AEO SEO strategies across all platforms.

Real-World Applications: Companies Getting Human-Centered AI Right

Several companies are already implementing Grieselhuber’s human-behavior-first approach with impressive results.

Shopify restructured their help documentation not for Google’s crawlers, but to match how merchants actually think about e-commerce problems. This human-centered approach improved their visibility across ChatGPT, Google, and traditional search simultaneously: a perfect example of effective LLM search engine optimization.

HubSpot analyzed thousands of customer service conversations to understand the natural language patterns people use when seeking marketing help. They then optimized their content to match these patterns, resulting in a 45% increase in AI-generated traffic referrals.

As one Reddit user in r/digital_marketing observed: “HubSpot’s content shows up everywhere now: Google, ChatGPT, you name it. They figured out that people ask the same questions whether they’re typing into a search bar or talking to an AI.”

The Data-Driven Approach to Human-Centered Optimization

Implementing Grieselhuber’s philosophy requires moving beyond traditional keyword research toward understanding behavioral patterns. This AI platform founder advocates for what he calls “intent archaeology”: digging deep into how people naturally express their needs.

Recent analysis from Conductor shows that successful AI-driven SEO strategies share three key characteristics:

• Natural Language Matching: Content that mirrors how people actually speak and think about problems
• Context Awareness: Understanding the user’s situation and expertise level
• Multi-Platform Consistency: Information that makes sense whether discovered through Google or generated by an LLM

Measuring Success Across AI Platforms

Traditional metrics like keyword rankings become less meaningful when optimizing for multiple AI platforms. Instead, focus on:

• Answer Engine Visibility: How often your content appears in AI-generated responses
• Intent Satisfaction Rates: Whether users find complete answers from your content
• Cross-Platform Performance: Consistency of visibility across Google, ChatGPT, and other AI tools

The Schema and Structured Data Reality Check

Many SEO professionals treat schema markup and structured data as magical solutions for AI optimization. However, this AI platform founder offers a more nuanced perspective: these technical elements influence LLMs, but they’re not magic bullets.

“Schema helps, but it’s not going to save content that doesn’t understand human behavior,” Grieselhuber explains. The key is using structured data to enhance already human-centered content, not as a substitute for understanding user intent.

A recent study by Search Engine Land found that pages with both strong user-focused content AND proper schema markup performed 23% better in AI-generated responses compared to pages with schema alone.

Beyond the Hype: Sustainable AI Strategy for 2025 and Beyond

While the industry buzzes about the latest AI developments, this AI platform founder’s approach offers something more valuable: sustainability. By focusing on human behavior rather than chasing every new LLM update, businesses can build search strategies that adapt to technological changes without requiring constant overhauls.

The most successful approaches to generative engine optimization will be those that use technology to better understand and serve human behavior, rather than replacing human considerations with purely algorithmic thinking.

Consider how Airbnb approaches this challenge. Instead of optimizing separately for Google and ChatGPT, they focus on understanding how travelers naturally describe their needs and preferences. This human-centered approach ensures their content performs well regardless of the search platform.

As more businesses recognize the importance of human behavior in SEO for AI tools, the competitive advantage will belong to those who can decode and serve authentic user intent across all platforms.

Companies looking to implement these strategies effectively often benefit from working with experienced SEO partners who understand both human psychology and technical optimization. Organizations like Dot Com Infoway specialize in developing comprehensive search strategies that account for both traditional and AI-driven search behaviors, ensuring long-term success in an evolving digital landscape.

The bottom line? While LLMs will continue to evolve, human nature remains remarkably consistent. Smart businesses will build their search strategies on this stable foundation rather than chasing every new AI trend.