The Matthew Effect

Definition: The Matthew Effect (also called cumulative advantage) describes how initial advantages compound over time—“the rich get richer.” In the context of AI search and Answer Engine Optimization (AEO), it shows up as big, already-visible brands being cited more often, which further increases their visibility while challenger brands are overlooked unless they supply unusually clear, citable answers.

Scope:

  • Includes: The original sociological concept (Merton, 1968); network effects like preferential attachment; modern manifestations in AI Overviews/answer engines where well-known sources are disproportionately selected.
  • Excludes: Purely moral or theological uses of “to whoever has, more will be given” without discussion of information, networks, or citations.
  • Notes: In AI search, the effect is not absolute—entity clarity, extractable formats, and evidence pointers can help challengers earn citations despite brand bias.

Why it matters: If you’re not already the category leader, you can’t count on “thought leadership” alone to be cited by answer engines. To counter the Matthew Effect, package content as definition docs, stepwise how-tos, comparison tables, calculators, or troubleshooters, use canonical entity names, and include credible references. This increases source eligibility and helps you win citations against larger incumbents.

See also: Answer Engine Optimization (AEO); AI Overviews; Entity; Source eligibility; Format discipline; AI-selectable paragraph; Micro-page; Comparison page; Calculator page; Preferential attachment

References:

Synonyms: Cumulative advantage; “rich-get-richer” effect; Preferential attachment (related)