AI SEO is not overhyped, but the way the industry talks about it often is. The experts in this SEOTalk Spaces episode argue that AI SEO only works when it builds on solid human insight, brand building and user-first content rather than replacing them.
Let’s explore the subject.
What Do We Really Mean By “AI SEO”?
Parth explained that most people use AI SEO as a catch-all term for three very different activities.
According to him, AI SEO typically includes:
Using AI to create or assist content at scale (including programmatic SEO)
Optimizing to get cited or featured in AI answers and overviews (AEO, GEO, LLM SEO)
Automating SEO tasks like research, clustering, and internal workflows with AI tools
Isha added that from a services point of view, AI SEO is simply “getting your website discovered and cited in AI engines like Google AI Overviews, ChatGPT, Gemini and others.”
Is AI SEO Actually Overhyped Right Now?
The panel agreed that the hype is real, but the traffic and attribution reality is still limited.
Malhar pointed out that for most brands, traffic attributable to LLMs is still in single digits, yet CXOs already feel FOMO and pressure their SEO teams to “optimize for AI.”
Deepak said the bigger pressure is not just “getting listed in AI,” but answering the client question: if we get cited, what do we actually gain and how do we measure it?
This has created:
Obsession with citations and screenshots instead of revenue
Overcomplication of SEO playbooks driven by fear, not user value
Parth’s view was clear: LLM visibility should be an outcome of good SEO, not a separate heroic goal.
How Is AI Really Changing SEO Strategy?
The guests repeatedly stressed that the fundamentals remain, but the execution layer is changing fast.
Key shifts they highlighted:
Customer journeys are now multi-touch: Reddit, communities, LLMs, search, social and ads all influence a single purchase
Organic is no longer guaranteed as the “first touch”; it may be an assist or last touch, which complicates measurement and reporting
Isha noted that marketers must now analyse:
Pre-site behaviour (where users first hear about the brand)
On-site behaviour (classic SEO analytics)
Post-exposure behaviour across channels
Brand recall and consistent entity signals across web, social, YouTube, podcasts and communities are becoming critical for both algorithms and AI systems.
Does Google Still Reward Human Content Over AI?
The entire conversation kept coming back to Google’s message: “our systems reward content made for people, not for search engines.”
Isha clarified that “human content” is not about a human typing every word but about adding genuine insight, opinion and context that only a practitioner or brand can provide.
Malhar and others linked this to uniqueness: content that simply synthesizes the web adds no new signal for LLMs or Google, while expert commentary, data, and lived experience are still rewarded.
Udit warned that “old content will die” if it is not refreshed regularly, and suggested:
Reviewing underperforming pages quarterly
Rewriting them with fresh context and updated insights instead of leaving them untouched for years
How Should SEOs Use AI Without Losing The Plot?
The panel shared practical ways to use AI for leverage rather than replacement.
Udit advocated “context engineering”:
Scrape niche data (e.g., top LinkedIn creators, your own tweets) and feed it into AI so outputs reflect unique perspectives, not generic web averages
Use tools and MCPs to integrate Ahrefs, APIs and browser data into AI workflows and make research 10x faster
On the operational side, Parth and Isha highlighted:
AI for research, clustering, internal linking suggestions and content briefs
Smaller teams running larger SEO programs with better margins, not just more headcount
At the same time, Udit reminded everyone that distribution is non‑negotiable: SEOs must now treat Medium, Substack, LinkedIn articles, YouTube, X and other platforms as parallel discovery surfaces that LLMs and Google both read heavily.
What are your thoughts on the topic? Share with us.
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