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AI search: what changed and what did not

A grounded guide to AI Overviews, answer engines, evidence, and the enduring work behind discoverability.

Direct answer

AI-shaped search changes how answers are assembled and how people move from an overview to sources, but the foundations remain: crawlable pages, index eligibility, clear original value, reliable evidence, and useful experiences.

Google states that no special AI markup, `llms.txt`, forced chunking, exhaustive query variants, or separate 'GEO' rewrite is required for its AI search features.

Practical steps

  1. Answer a real task directly, then provide methods, evidence, nuance, and useful next actions.
  2. Make original observations, datasets, tools, or expert processes citable and understandable.
  3. Keep important information in accessible HTML with accurate structured data only where visible content supports it.
  4. Measure query/page coverage and assisted outcomes rather than inventing an AI visibility score.

Example

Publishing a transparent experiment with data, method, and limitations creates more reusable evidence than rewriting the same generic explanation into dozens of question variants.

Common mistakes

  • Treating `llms.txt` as a Google ranking tactic.
  • Inventing special GEO schema.
  • Scaling thin AI summaries or fake citations to simulate topical authority.

Limitations

  • AI interfaces and reporting change quickly, and source inclusion is not guaranteed.
  • Attribution can be indirect and measurement remains incomplete.

Primary sources

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