Is SEO Still Viable for AI-Powered Search?

SEO finds itself at a pivotal moment, with experts split between optimizing for large language models (LLMs) and sticking with traditional methods. A more balanced path exists, however, one that merges SEO fundamentals with a deeper understanding of how AI-powered search actually works.

By blending what has always worked,like on-page optimization and high-quality backlinks,with forward-looking tactics such as adapting to query fan-out and emerging prompt intents, you can stay relevant. Since 2023, with the rise of ChatGPT, Gemini, Claude, and Perplexity, I have tracked how AI engines display results and where SEO is heading. Here is what I have learned, and how you can rethink your strategy for a future where AI SEO is rooted in human behavior.

The Red Queen theory offers a useful lens for AI search. It posits that to stay in the same place, you must constantly evolve. As you adapt, so do your competitors, keeping the gap between you constant. If you do not change, you risk being left behind. Your customers search everywhere, so your brand must appear everywhere.

AI search is not a sudden revolution but a natural progression from hybrid models like RankBrain, introduced in 2015. Many classic SEO tactics still work because change has been gradual. LLMs still rely on retrieval-based engines. Content quality and freshness matter. Site speed is critical. Intent matching across major categories remains relevant. As Britney Muller notes, “Stop optimizing for ‘AI.’ Optimize for search engines so retrieval-based AI can cite you, and earn third-party coverage so the model knows you before the prompt is typed.”

What makes a source worthy for LLMs? Research from Moz shows only 12% of AI Mode citations match organic URLs, meaning AI engines only partly follow traditional SEO rules. While Google denies a fully generative search engine, I predict it will continue integrating AI assistant behaviors like questions, actions, analysis, and creation. Your short- and long-term strategies must work together to stay innovative yet grounded. Focusing on human behavior and traditional search while understanding LLMs is how you honor the Red Queen.

Retrieval-augmented generation (RAG) is essential to grasping AI search. LLMs have limited databases, so they use RAG to avoid hallucinations and provide relevant answers. For instance, when I asked Google AI Mode and ChatGPT for an anti-aging skincare routine, both returned relevant results but from different sources. Google used dermatology sites and shopping listings; ChatGPT preferred a fresh Today.com article. Both needed external sources.

To optimize for AI versus traditional search, shift your thinking from crafting SEO content to becoming a trusted supplier for LLMs. When people enter a prompt, your brand should appear in the answer. This involves studying AI answers, learning their quirks, comparing preferences, and evaluating common intents. Then, feed the database. Ensure Google, with its vast LLM database, has enough data to keep you in its pool of trusted sources. Without people, AI assistants have no power, so put people first. Ask where people use AI to create, achieve, build, search, and prompt, and where your brand fits.

Short-term SEO tactics rely on topical authority. As Kevin Indig explains, “Internal links aren’t just distributing authority. They’re defining the semantic structure of your site.” Vector search models, like AI Mode, use entities and internal links to strengthen signals. Gianluca Fiorelli suggests linking internally and externally to reinforce entity connections, helping LLMs map embeddings to a wider network. Plan topical authority through four lenses: topical coverage, query fan-out, intent, and content quality following E-E-A-T principles. These are traditional tactics adapted for hybrid or LLM-based approaches.

Technical health remains rooted in what works: site speed, schema markup, and optimized titles and descriptions. LLMs are expensive to run, so they prefer fast, easy-to-extract resources. Mike King notes that “slow responses can trigger 499 errors, where the AI stops waiting.” These short-term goals,topical coverage, internal links, and technical health,are vital for LLM visibility.

Long-term strategies must focus on human behavior. Identify search intent beyond the traditional four. MIT lists zero-shot, instructional, and contextual prompts; Grammarly includes educational, opinion-based, and problem-solving intents. I break intent into categories like directional, recommendation, local, booking, and shopping. Then, consider query fan-out. For example, if your target customers are U. S. home buyers asking “Is now a good time to buy a house?” study AI answers. AI Mode fans out across market conditions and pros and cons; ChatGPT looks at trends and seasonality. Develop a content strategy that supports this behavior, using tools like Semrush, Ahrefs, or Profound.

In the future, AI assistants may anticipate needs rather than respond to prompts, according to Harvard Business Review. Focus less on prompts and more on problems. Analyze how people use AI assistants, thinking creatively like social media professionals. Rare Beauty and Rhode have grown through brand campaigns, PR, TikTok, and IRL experiences. When asked for “best makeup gifts for Gen Z,” ChatGPT recommends Rare Beauty first, influenced by PR and social virality.

SEO will endure as long as search engines with AI experiences exist. It may look like SEO has become the prey, but it has evolved just as much as the predator. Everything has changed, yet everything remains the same.