Team Structure for AEO Success

Ethan Smith views answer engine optimization (AEO) as the second most significant shift in search history, representing an evolution rather than a replacement of traditional SEO principles. His perspective is grounded in nearly two decades of experience, having witnessed the transition from spam-based SEO to quality-focused content.

The core of Smith's philosophy is that anything can be optimized once you understand the underlying systems and rules. With AEO, this means recognizing that while traditional SEO tactics still work, there are additional strategies unique to answer engines that can drive significant results.

Smith emphasizes that AEO differs fundamentally from SEO in how content wins visibility. In traditional search, ranking #1 guarantees visibility, but in answer engines, frequency of citation across multiple sources determines prominence. This creates a democratized landscape where even early-stage companies can compete effectively by generating mentions across diverse platforms.

This citation-based approach requires a different optimization strategy. Smith recommends focusing on two parallel tracks: on-site optimization (traditional SEO with emphasis on answering follow-up questions) and off-site citation building (getting mentioned across YouTube, Reddit, affiliate sites, and other sources). The data shows this approach works—Webflow saw a 6x higher conversion rate from LLM traffic compared to traditional search traffic.

For implementation, Smith advocates an experimental mindset. He recommends selecting questions to target, tracking performance with specialized tools, analyzing citation sources, creating comprehensive landing pages, optimizing off-site presence, and measuring results through controlled experiments. This methodical approach helps identify what actually works in a field where, as Smith notes, "most work is wasted" and "most best practices are not correct."

Smith's perspective on AI-generated content is particularly insightful. Despite industry excitement, his research shows that purely AI-generated content (without human editing) performs poorly in both search and answer engines. He warns of "model collapse" where AI systems trained on AI-generated content create an "infinite loop of derivatives" that converges on singular, less valuable opinions.

For leaders building teams, Smith suggests pairing SEO specialists with community-oriented marketers who can handle Reddit engagement and video content creation—skills traditional SEO teams may lack. For early-stage companies, he recommends focusing exclusively on citation optimization and long-tail questions rather than traditional SEO approaches.

The practical implication is clear: AEO represents a significant opportunity that requires intentional optimization, but success depends on understanding the unique dynamics of answer engines rather than simply applying traditional SEO tactics or generating AI content at scale.