7 Questions With Ayub Ansary on AI, LLMs, SEO and User Experience

Search is no longer a quiet exchange between a query and a list of links. It is becoming a conversation, mediated by large language models that decide what to explain, what to omit, and which sources deserve attention. For businesses, publishers, and product teams, this shift raises hard questions about visibility, trust, and relevance.

Ayub Ansary, a senior SEO strategist working at the intersection of AI systems, search behavior, and user experience, has spent the past few years studying how large language models interpret content and how that interpretation alters discovery. In this conversation, he discusses what is changing, what still matters, and where most brands are falling behind.

Q1. How are large language models changing the fundamentals of search?

Ayub Ansary: They change who the decision-maker is. Traditional search rewarded pages that matched queries and earned signals like links and engagement. LLMs operate one step higher. They interpret intent, compress information, and act as a filter between the user and the open web.

That means visibility no longer depends only on ranking. It depends on whether your content is selected, summarized, or referenced by the model. Many pages still exist online, but fewer are truly seen.

Q2: Does this signal the end of classic SEO?

Ayub Ansary: No, but it marks the end of mechanical SEO. Tactics built around repetition, shallow optimization, or gaming structure lose value fast. What remains is strategic SEO rooted in clarity, authority, and usefulness.

Search has moved closer to how humans judge information. Models reward pages that explain things clearly, cover real constraints, and reflect lived experience. SEO now looks less like a checklist and more like editorial judgment.

Q3: What happens to keywords in this environment?

Ayub Ansary: Keywords stop acting like targets and start acting like context signals. They help models identify the subject, but they do not carry the conversation.

What matters more is semantic completeness. A page that answers a question fully, anticipates follow-ups, and resolves confusion performs better than a page tightly wrapped around one phrase. Models read for meaning, not density.

Q4: How does user experience factor into LLM-driven search?

Ayub Ansary: User experience becomes part of discovery, not just conversion. Poor structure, confusing flow, or vague language signals low confidence to both users and models.

Clear headings, logical sequencing, and direct answers help models extract value. If a page is hard for a human to follow, it is hard for a model to trust. UX now influences whether content is cited at all.

Q5: What signals seem to matter more now than before?

Ayub Ansary: Specificity and consistency. Models respond well to content that shows decision-making, trade-offs, and boundaries. Generic advice struggles.

A clear voice matters too. Content written with confidence and internal logic tends to be reused more often than content stitched together from trends. LLMs favor material that sounds like it comes from someone who has done the work.

Q6: Many publishers worry AI will erase their traffic. Is that fear valid?

Ayub Ansary: Traffic will redistribute. Basic informational queries will generate fewer clicks, but demand for trusted sources grows.

Brands that become reference points still attract attention, leads, and authority. AI removes noise, not value. The loss happens mostly to content that never offered depth in the first place.

Q7: What separates brands that will stay visible from those that will fade?

Ayub Ansary: The ability to explain how and why, not just what. Brands that document their thinking, share real insight, and respect user intent continue to surface in AI answers.

Those that rely on surface-level optimization slowly disappear from the conversation. LLMs reward substance over signals. The future favors people who understand their field and communicate it plainly.

As search becomes more mediated, the rules of visibility shift from technical tricks to editorial substance. For Ansary, the message is direct: write for humans, structure for clarity, and earn trust through depth.

What emerges is a quieter but more demanding standard. Content is judged less by how loudly it signals relevance and more by how well it explains reality. The web, filtered through AI systems, favors sources that reduce confusion rather than amplify noise. In that environment, credibility is built sentence by sentence, decision by decision.

The machines may be watching closely, but they are learning from what people have always valued: clear thinking, honest answers, and work that holds up when summarized.