SEO for AI search: Clear Takeaways from John Mueller and Danny Sullivan

Google's John Mueller and Danny Sullivan made one thing crystal clear in their recent Search Off the Record podcast: SEO for AI-powered search is just SEO. The acronyms keep changing (GEO, AIO, whatever comes next), but the actual work—creating original, authentic, user-focused content—remains exactly the same. What's changing is not the strategy, but the format and how success is measured.

Understanding the SEO Hierarchy: There's No "AI SEO"

One of the most important clarifications Danny Sullivan made on the podcast is this: there is no separate "AI SEO" discipline. Instead, he frames optimization for AI-powered search as a subset of traditional SEO—just as local SEO, voice search optimization, and mobile SEO before it were all subsets of the broader SEO umbrella.

When clients ask SEO professionals for "AI optimization," what they're really asking for is the ability to succeed in a new search format. But this doesn't mean abandoning everything you know about SEO. The fundamentals remain unchanged: understanding user intent, creating quality content, and optimizing for human satisfaction. The difference is that the results are now presented in a different format—conversational, multimodal, and context-aware—rather than traditional blue links.

Core principles that stay the same

• Serve user intent.

• Produce useful, original content.

• Build clear site structure and good technical hygiene.

• Focus on quality engagement and conversions, not raw clicks.

What AI search changes

• Results often show a summarized AI answer up top.

• Users get richer previews before they click.

• Queries branch into related subquestions automatically.

• Different formats gain visibility, like video and audio.

• Search engines will infer content more reliably from pages.

This reframing is crucial for client conversations. Instead of positioning AI search as a completely new challenge that requires starting from scratch, SEO professionals can confidently say: "The long-term strategy is exactly the same as it's always been. We're not changing what we do; we're adapting how formats are presented."

Why Technical SEO Is No Longer the Bottleneck

For years, technical SEO was a major focus area—ensuring sites were crawlable, mobile-friendly, fast, and well-structured. John Mueller highlighted an important shift in this area: modern content management systems (CMS) now have technical SEO built in by default. The days of spending significant resources on technical fixes for individual search engines are largely over.

Technical SEO today

• Most CMSs handle basic technical SEO.

• Continue to check crawlability, index signals, canonicals, sitemaps, and status codes.

• Test JavaScript rendering for pages that rely on client side content.

• Ensure accessibility and semantic HTML, this helps AI systems parse your content.

This represents a fundamental change in how SEO professionals should allocate their efforts. Rather than obsessing over site speed, XML sitemaps, robots.txt, structured data minutiae, and mobile responsiveness (most of which are now handled automatically), teams should focus the bulk of their attention on content quality and originality. The technical foundation is already there for most modern websites.

This evolution mirrors earlier shifts in search history. Mueller explained that in the early days of search, professionals would sometimes create different versions of content for different search engines, even though the differences weren't significant enough to justify the effort. Over time, as search engines converged on standards and web development best practices matured, this effort became unnecessary. The same pattern is now happening with AI search—the technical baseline is being raised, so professionals can stop worrying about engine-specific optimization and focus on the user.

Original Content as the Ultimate Competitive Advantage

At the core of Google's guidance for AI search is this simple truth: AI systems are exceptionally good at finding, synthesizing, and presenting non-original content. Every year, publishers create thousands of nearly identical articles answering the same basic questions—"What time is the Super Bowl?" being the classic example. AI systems can handle commodity content with ease.

What AI systems cannot do well is create or replace original, authentic content rooted in real experience. This is where genuine SEO professionals and creators have a sustainable advantage. Sullivan specifically highlighted several categories of content that consistently outperform commodity content:

Practical content rules

• Prioritize originality. Repetitive factual pages lose value.

• Add first hand reporting, experiments, interviews, or unique data.

• Offer expert takes and opinions tied to real experience.

• Publish multiple formats for the same topic: longform article, short video, audio snippet, data table.

• Keep headings clear and answer-focused. Use structured data where it helps.

The distinction is important: authentic content cannot be artificially created at scale. This doesn't mean AI tools can't be useful in the content creation process—they can help with research, outlining, and drafting. But the final content must be reviewed, enhanced, and authenticated by a human expert with genuine knowledge or experience in the subject matter.

This shift from keyword-driven content creation to experience-driven content creation represents the most significant strategic change for SEO teams in the AI era. Instead of asking "What keywords should this page target?", the better question becomes "What unique perspective, expertise, or experience can we contribute to this topic that no one else can provide?".

Authenticity: The New Currency of Search

Related to originality, but distinct from it, is authenticity. Sullivan used an interesting example to illustrate this: he observes what resonates with people on social media—the content, creators, and voices that get shared, commented on, and recommended. The pattern is clear: people engage with creators and brands that feel genuine, consistent, and grounded in reality. This authenticity has to be real. You cannot manufacture it at scale with AI. A brand that attempts to create hundreds of "authentic-sounding" pieces without genuine connection to a real voice, perspective, or experience will struggle to build trust. Conversely, a creator or brand that consistently demonstrates their actual expertise, values, and personality will naturally attract an audience that trusts them.

For SEO professionals working with clients, this suggests a shift in positioning. Rather than having clients compete on production volume (churning out more content), help them compete on authenticity and consistency—developing a clear, recognizable voice and perspective that only they can provide. This is more defensible against AI-generated alternatives and more likely to build long-term audience loyalty.

E-E-A-T Becomes Even More Important

Google's expanded emphasis on E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) becomes even more critical in the AI search era. The addition of the first "E" for Experience—emphasizing first-hand, direct knowledge—directly addresses the authenticity issue. Content created by people with genuine experience in the topic is increasingly valued. This doesn't mean every piece of content needs to be written by someone with decades of experience. But it does mean that wherever possible, you should signal real expertise and experience. This can be done through author bios that highlight genuine credentials, transparent discussion of methodologies and personal experience, and citing of reputable sources that validate the content's foundation.

In the AI search context, E-E-A-T signals help AI systems distinguish between commodity content (which anyone with internet access can create) and content backed by genuine expertise and experience.

Multimodal Content: Moving Beyond Text-Only Strategy

Sullivan made a humorous observation while discussing multimodal content: he actually dislikes the term "multimodal"—it sounds technical and jargon-heavy. His preferred framing is simpler: "You search one way and get information back another way."

What this means practically is that users expect to find information in the format that works best for their context and learning style. Some users want a detailed blog post. Others want a quick video. Still others want to listen to audio while driving. If your content only exists in text format, you're invisible to users looking for video explanations or audio discussions.

The shift toward multimodal content isn't just about serving user preferences—it's also about how AI search works. Modern AI systems analyze and cite content across multiple formats. A brand that appears only as text in search results is less likely to be cited by AI systems than a brand that provides the same information in text, video, and audio formats. This multiplies visibility opportunities.

Format strategy

• Repurpose core research into: article, key takeaways, short video, podcast clip, data download.

• Label formats clearly for both users and machines.

• Test which formats deliver higher conversion and retention for each content type.

For content teams, the practical approach is to start with your strongest format and multiply from there. If written content is your strength, identify the 3-5 pieces that perform best, then systematically create video, audio, and visual versions of that content. The goal isn't to be equally good at all formats—it's to be present across formats in a strategic way that extends your content's reach and lifespan.

Quality Metrics Over Volume

One of the most significant changes in how success is measured relates to engagement and conversion quality rather than raw traffic or rankings. Sullivan pointed out that with traditional search, a click is a click—your analytics counted every visit equally. With AI search, the context around that click matters much more.

Tracking and measurement

• Track quality of traffic, not just volume. Use engagement metrics and conversion rates.

• Add events for user actions inside pages and multimedia.

• Use session recordings and heatmaps alongside traditional analytics to see user intent.

• Expect search console updates over time. Until then, combine sources to map query-to-content performance.

Quality clicks—those from users who have higher intent and context—are significantly different from commodity clicks. AI search's ability to run multiple related queries behind the scenes and synthesize results gives users more context before they ever click. This means that when someone does click through from an AI result, they're more likely to actually convert or engage meaningfully. Conversely, some clicks that traditional search sent your way might have been from confused users who bounced immediately.

This shift changes how success should be measured. Instead of focusing on rankings and click volume, focus on:

• ​Dwell time: How long users spend on your pages (indicating content relevance and satisfaction)

• ​Engagement metrics: Scroll depth, pages per session, time on site—all signals that users found what they needed

• ​Conversion quality: Not just whether someone converted, but whether they became a valuable customer or engaged reader

• ​Query intent satisfaction: Whether your content actually answered the question the user was asking

Mueller also touched on something called query fan-out—the fact that AI search systems do multiple searches behind the scenes to synthesize an answer. This means traditional "I rank for this keyword but not in AI results" comparisons are flawed. The AI system might be using your content even though it's not visible under the exact query the user typed. This suggests that visibility tracking for AI search will eventually require different tools and metrics than those used for traditional search.

Better Tracking for SEO for AI Search

Danny Sullivan acknowledged a gap that needs to be filled: current Search Console tools don't provide clear visibility into how your content performs in AI search results. He mentioned that Google is working on Search Console improvements to help site owners understand which queries surface their content in AI results and whether their content is being cited. Recent Google updates confirm this direction. In December 2025, Google introduced AI-powered configuration in Search Console's Performance report, allowing natural-language queries to set up analysis (e.g., "Compare blog traffic this quarter vs last year") without manual filtering. Google also began testing social channels visibility inside Search Console Insights, acknowledging that organic visibility now spans beyond website pages to include social profiles and video channels.

These updates suggest that the future of Search Console will increasingly help SEO professionals understand their content's visibility across different surfaces—not just traditional blue links, but AI Overviews, answer engines, social profiles, and other emerging formats. For now, success tracking requires looking at multiple data sources: Search Console for core query and ranking data, Google Analytics for engagement signals, and platform-specific analytics for video, podcasts, and social content.

Addressing Client Anxiety: Long-Term Strategy Hasn't Changed

Sullivan and Mueller both acknowledged a real challenge that SEO professionals face: clients are anxious about AI search and want to know they're doing "the new stuff". When teams say "Keep doing what you've been doing," it can feel dismissive or like the agency isn't adapting to change. The reframing that works is this: The long-term strategy remains unchanged, but we're evolving the tactics to accommodate new formats. You're not asking clients to stop creating quality content; you're asking them to think about how that content is presented and consumed.

The investment in original, authentic, multimodal content isn't new—it's deepening a strategy that's always worked.

This also means that shortcuts and quick wins won't last, just as they didn't in traditional SEO. Clients who try to game AI search with manipulative tactics or thin optimization techniques will find diminishing returns as quickly as they do with traditional search. The sustainable approach is always the same: create content that genuinely serves users, be authentic, offer original value, and let search engines—in whatever format—surface that quality content.

Moving Forward

Based on the guidance from Mueller and Sullivan, we should focus on these core activities:

• Audit top ranking pages for originality and first hand value.

• Add at least one piece of unique data or insight per pillar page.

• Create short-form video or audio for your best performing posts.

• Verify server side rendering or prerendering for JS heavy pages.

• Instrument events for clicks, time on section, video plays, form starts.

• Monitor conversions from search traffic by query group when possible.

Client Communication

• Reframe AI search as evolution, not revolution

• Explain that the fundamentals haven't changed, but formats and presentation are evolving

• Position multimodal content strategy as deepening an existing content investment, not starting from scratch

• Educate clients that shortcuts won't work—long-term, authentic content creation is the sustainable path

The message from Google is clear: stop chasing new acronyms and stop reinventing your strategy for each new search format. Instead, double down on the principles that have always worked in search—understanding users, creating quality content, being authentic, and measuring what matters. That's the true competitive advantage in the AI search era, and it's the same advantage that mattered in traditional search.